h&b       !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~                                                                                                                                                                    !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""##################################$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%&&&&&&&&&&&&&&&&&&& & & & & & & & & & & & & & & & & & & & & & & & & & & & & & & & & ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) * * * * * * * * * * * * * * * * * * * * * * * * * * * * + + + + + + + + + + + + + + + + + + + + + + + + + + + + , , , , , , , , , , , , , , , , , , , , , , , , , , , , - - - - - - - - - - - - - - - - - - - - - - - - - - - - . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 9 9 9 9 9 9 9 9 9 9 9999999999999999999999::::<(c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred";?$ amazonka-mlThe function used to train an MLModel. Training choices supported by Amazon ML include the following:SGD - Stochastic Gradient Descent. RandomForest# - Random forest of decision trees.(c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred";?( amazonka-ml:A list of the variables to use in searching or filtering BatchPrediction. CreatedAt - Sets the search criteria to BatchPrediction creation date.Status - Sets the search criteria to BatchPrediction status.Name4 - Sets the search criteria to the contents of BatchPrediction Name.IAMUser - Sets the search criteria to the user account that invoked the BatchPrediction creation. MLModelId# - Sets the search criteria to the MLModel used in the BatchPrediction. DataSourceId# - Sets the search criteria to the  DataSource used in the BatchPrediction.DataURI - Sets the search criteria to the data file(s) used in the BatchPrediction. The URL can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory. ! ! ! (c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred";?,5 amazonka-ml9A list of the variables to use in searching or filtering  DataSource. CreatedAt - Sets the search criteria to  DataSource creation date.Status - Sets the search criteria to  DataSource status.Name/ - Sets the search criteria to the contents of  DataSource Name.DataUri - Sets the search criteria to the URI of data files used to create the  DataSource. The URI can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory.IAMUser - Sets the search criteria to the user account that invoked the  DataSource creation.Note:< The variable names should match the variable names in the  DataSource. 5=<;:98675=<;:9867=<;:98(c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred";?.#Q amazonka-mlContains the key values of  DetailsMap:PredictiveModelType - Indicates the type of the MLModel. Algorithm6 - Indicates the algorithm that was used for the MLModel.QUTRSQUTRSUT(c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred";?/Oi amazonka-ml1Object status with the following possible values: PENDING  INPROGRESS FAILED  COMPLETED DELETEDiponmljk iponmljkponml(c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred"%&';Jv amazonka-mlRepresents the output of a GetBatchPrediction operation.The content consists of the detailed metadata, the status, and the data file information of a Batch Prediction.See:  smart constructor. amazonka-mlThe ID of the  DataSource6 that points to the group of observations to predict. amazonka-mlThe ID assigned to the BatchPrediction at creation. This value should be identical to the value of the BatchPredictionID in the request. amazonka-mlThe time that the BatchPrediction3 was created. The time is expressed in epoch time. amazonka-ml&The AWS user account that invoked the BatchPrediction. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account. amazonka-mlThe location of the data file or directory in Amazon Simple Storage Service (Amazon S3). amazonka-ml(The time of the most recent edit to the BatchPrediction'. The time is expressed in epoch time. amazonka-mlThe ID of the MLModel% that generated predictions for the BatchPrediction request. amazonka-mlA description of the most recent details about processing the batch prediction request. amazonka-ml+A user-supplied name or description of the BatchPrediction. amazonka-mlThe location of an Amazon S3 bucket or directory to receive the operation results. The following substrings are not allowed in the s3 key portion of the  outputURI" field: ':', '//', '/./', '/../'. amazonka-mlThe status of the BatchPrediction5. This element can have one of the following values:PENDING - Amazon Machine Learning (Amazon ML) submitted a request to generate predictions for a batch of observations. INPROGRESS - The process is underway.FAILED - The request to perform a batch prediction did not run to completion. It is not usable. COMPLETED7 - The batch prediction process completed successfully.DELETED - The BatchPrediction- is marked as deleted. It is not usable. amazonka-mlCreate a value of " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility:,  - The ID of the  DataSource6 that points to the group of observations to predict.,  - The ID assigned to the BatchPrediction at creation. This value should be identical to the value of the BatchPredictionID in the request.,  - Undocumented member.,  - The time that the BatchPrediction3 was created. The time is expressed in epoch time., ) - The AWS user account that invoked the BatchPrediction. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.,  - Undocumented member.,  - The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).,  - Undocumented member., + - The time of the most recent edit to the BatchPrediction'. The time is expressed in epoch time.,  - The ID of the MLModel% that generated predictions for the BatchPrediction request.,  - A description of the most recent details about processing the batch prediction request., . - A user-supplied name or description of the BatchPrediction.,  - The location of an Amazon S3 bucket or directory to receive the operation results. The following substrings are not allowed in the s3 key portion of the  outputURI" field: ':', '//', '/./', '/../'.,  - Undocumented member.,  - The status of the BatchPrediction5. This element can have one of the following values:PENDING - Amazon Machine Learning (Amazon ML) submitted a request to generate predictions for a batch of observations. INPROGRESS - The process is underway.FAILED - The request to perform a batch prediction did not run to completion. It is not usable. COMPLETED7 - The batch prediction process completed successfully.DELETED - The BatchPrediction- is marked as deleted. It is not usable.,  - Undocumented member. amazonka-mlThe ID of the  DataSource6 that points to the group of observations to predict. amazonka-mlThe ID assigned to the BatchPrediction at creation. This value should be identical to the value of the BatchPredictionID in the request. amazonka-mlUndocumented member. amazonka-mlThe time that the BatchPrediction3 was created. The time is expressed in epoch time. amazonka-ml&The AWS user account that invoked the BatchPrediction. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account. amazonka-mlUndocumented member. amazonka-mlThe location of the data file or directory in Amazon Simple Storage Service (Amazon S3). amazonka-mlUndocumented member. amazonka-ml(The time of the most recent edit to the BatchPrediction'. The time is expressed in epoch time. amazonka-mlThe ID of the MLModel% that generated predictions for the BatchPrediction request. amazonka-mlA description of the most recent details about processing the batch prediction request. amazonka-ml+A user-supplied name or description of the BatchPrediction. amazonka-mlThe location of an Amazon S3 bucket or directory to receive the operation results. The following substrings are not allowed in the s3 key portion of the  outputURI" field: ':', '//', '/./', '/../'. amazonka-mlUndocumented member. amazonka-mlThe status of the BatchPrediction5. This element can have one of the following values:PENDING - Amazon Machine Learning (Amazon ML) submitted a request to generate predictions for a batch of observations. INPROGRESS - The process is underway.FAILED - The request to perform a batch prediction did not run to completion. It is not usable. COMPLETED7 - The batch prediction process completed successfully.DELETED - The BatchPrediction- is marked as deleted. It is not usable. amazonka-mlUndocumented member.##(c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred";?N amazonka-ml9A list of the variables to use in searching or filtering  Evaluation. CreatedAt - Sets the search criteria to  Evaluation creation date.Status - Sets the search criteria to  Evaluation status.Name/ - Sets the search criteria to the contents of  Evaluation ____ Name.IAMUser - Sets the search criteria to the user account that invoked an evaluation. MLModelId# - Sets the search criteria to the  Predictor that was evaluated. DataSourceId# - Sets the search criteria to the  DataSource used in evaluation.DataUri - Sets the search criteria to the data file(s) used in evaluation. The URL can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory. (c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred";?O  (c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred";?P  (c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred"%&';Vz amazonka-mlMeasurements of how well the MLModel performed on known observations. One of the following metrics is returned, based on the type of the MLModel:BinaryAUC: The binary MLModel uses the Area Under the Curve (AUC) technique to measure performance.RegressionRMSE: The regression MLModel uses the Root Mean Square Error (RMSE) technique to measure performance. RMSE measures the difference between predicted and actual values for a single variable.$MulticlassAvgFScore: The multiclass MLModel9 uses the F1 score technique to measure performance.For more information about performance metrics, please see the  6https://docs.aws.amazon.com/machine-learning/latest/dg'Amazon Machine Learning Developer Guide.See:  smart constructor. amazonka-mlCreate a value of " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility:,  - Undocumented member. amazonka-mlUndocumented member. (c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred"%&';u amazonka-mlRepresents the output of  GetEvaluation operation.The content consists of the detailed metadata and data file information and the current status of the  Evaluation.See:  smart constructor. amazonka-mlThe time that the  Evaluation3 was created. The time is expressed in epoch time. amazonka-mlThe AWS user account that invoked the evaluation. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account. amazonka-mlThe ID of the  DataSource that is used to evaluate the MLModel. amazonka-mlThe ID that is assigned to the  Evaluation at creation. amazonka-mlThe location and name of the data in Amazon Simple Storage Server (Amazon S3) that is used in the evaluation. amazonka-ml(The time of the most recent edit to the  Evaluation'. The time is expressed in epoch time. amazonka-mlThe ID of the MLModel% that is the focus of the evaluation. amazonka-ml>A description of the most recent details about evaluating the MLModel. amazonka-ml+A user-supplied name or description of the  Evaluation. amazonka-mlMeasurements of how well the MLModel2 performed, using observations referenced by the  DataSource. One of the following metrics is returned, based on the type of the MLModel:BinaryAUC: A binary MLModel uses the Area Under the Curve (AUC) technique to measure performance.RegressionRMSE: A regression MLModel uses the Root Mean Square Error (RMSE) technique to measure performance. RMSE measures the difference between predicted and actual values for a single variable."MulticlassAvgFScore: A multiclass MLModel9 uses the F1 score technique to measure performance.For more information about performance metrics, please see the  6https://docs.aws.amazon.com/machine-learning/latest/dg'Amazon Machine Learning Developer Guide. amazonka-mlThe status of the evaluation. This element can have one of the following values:PENDING - Amazon Machine Learning (Amazon ML) submitted a request to evaluate an MLModel. INPROGRESS - The evaluation is underway.FAILED - The request to evaluate an MLModel2 did not run to completion. It is not usable. COMPLETED1 - The evaluation process completed successfully.DELETED - The  Evaluation( is marked as deleted. It is not usable. amazonka-mlCreate a value of " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility:,  - Undocumented member.,  - The time that the  Evaluation3 was created. The time is expressed in epoch time.,  - The AWS user account that invoked the evaluation. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.,  - The ID of the  DataSource that is used to evaluate the MLModel., " - The ID that is assigned to the  Evaluation at creation.,  - Undocumented member.,  - The location and name of the data in Amazon Simple Storage Server (Amazon S3) that is used in the evaluation., + - The time of the most recent edit to the  Evaluation'. The time is expressed in epoch time.,  - The ID of the MLModel% that is the focus of the evaluation.,  - A description of the most recent details about evaluating the MLModel., . - A user-supplied name or description of the  Evaluation.,  - Measurements of how well the MLModel2 performed, using observations referenced by the  DataSource. One of the following metrics is returned, based on the type of the MLModel:BinaryAUC: A binary MLModel uses the Area Under the Curve (AUC) technique to measure performance.RegressionRMSE: A regression MLModel uses the Root Mean Square Error (RMSE) technique to measure performance. RMSE measures the difference between predicted and actual values for a single variable."MulticlassAvgFScore: A multiclass MLModel9 uses the F1 score technique to measure performance.For more information about performance metrics, please see the  6https://docs.aws.amazon.com/machine-learning/latest/dg'Amazon Machine Learning Developer Guide.,  - Undocumented member.,  - The status of the evaluation. This element can have one of the following values:PENDING - Amazon Machine Learning (Amazon ML) submitted a request to evaluate an MLModel. INPROGRESS - The evaluation is underway.FAILED - The request to evaluate an MLModel2 did not run to completion. It is not usable. COMPLETED1 - The evaluation process completed successfully.DELETED - The  Evaluation( is marked as deleted. It is not usable. amazonka-mlUndocumented member. amazonka-mlThe time that the  Evaluation3 was created. The time is expressed in epoch time. amazonka-mlThe AWS user account that invoked the evaluation. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account. amazonka-mlThe ID of the  DataSource that is used to evaluate the MLModel. amazonka-mlThe ID that is assigned to the  Evaluation at creation. amazonka-mlUndocumented member. amazonka-mlThe location and name of the data in Amazon Simple Storage Server (Amazon S3) that is used in the evaluation. amazonka-ml(The time of the most recent edit to the  Evaluation'. The time is expressed in epoch time. amazonka-mlThe ID of the MLModel% that is the focus of the evaluation. amazonka-ml>A description of the most recent details about evaluating the MLModel. amazonka-ml+A user-supplied name or description of the  Evaluation. amazonka-mlMeasurements of how well the MLModel2 performed, using observations referenced by the  DataSource. One of the following metrics is returned, based on the type of the MLModel:BinaryAUC: A binary MLModel uses the Area Under the Curve (AUC) technique to measure performance.RegressionRMSE: A regression MLModel uses the Root Mean Square Error (RMSE) technique to measure performance. RMSE measures the difference between predicted and actual values for a single variable."MulticlassAvgFScore: A multiclass MLModel9 uses the F1 score technique to measure performance.For more information about performance metrics, please see the  6https://docs.aws.amazon.com/machine-learning/latest/dg'Amazon Machine Learning Developer Guide. amazonka-mlUndocumented member. amazonka-mlThe status of the evaluation. This element can have one of the following values:PENDING - Amazon Machine Learning (Amazon ML) submitted a request to evaluate an MLModel. INPROGRESS - The evaluation is underway.FAILED - The request to evaluate an MLModel2 did not run to completion. It is not usable. COMPLETED1 - The evaluation process completed successfully.DELETED - The  Evaluation( is marked as deleted. It is not usable. (c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred"%&';}G amazonka-mlThe output from a Predict operation:Details+ - Contains the following attributes: DetailsAttributes.PREDICTIVE_MODEL_TYPE - REGRESSION | BINARY | MULTICLASS !DetailsAttributes.ALGORITHM - SGDPredictedLabel - Present for either a BINARY or  MULTICLASS MLModel request.PredictedScores - Contains the raw classification score corresponding to each label.PredictedValue - Present for a  REGRESSION MLModel request.See:  smart constructor. amazonka-ml"The prediction label for either a BINARY or  MULTICLASS MLModel. amazonka-mlThe prediction value for  REGRESSION MLModel. amazonka-mlCreate a value of " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility:,  - Undocumented member., % - The prediction label for either a BINARY or  MULTICLASS MLModel.,  - Undocumented member.,  - The prediction value for  REGRESSION MLModel. amazonka-mlUndocumented member. amazonka-ml"The prediction label for either a BINARY or  MULTICLASS MLModel. amazonka-mlUndocumented member. amazonka-mlThe prediction value for  REGRESSION MLModel.   (c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred"%&'; amazonka-ml/The database details of an Amazon RDS database.See:  smart constructor. amazonka-mlThe ID of an RDS DB instance. amazonka-mlCreate a value of " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility:,  - The ID of an RDS DB instance.,  - Undocumented member. amazonka-mlThe ID of an RDS DB instance. amazonka-mlUndocumented member. amazonka-ml amazonka-ml(c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred"%&';y amazonka-mlThe database credentials to connect to a database on an RDS DB instance.See:  smart constructor. amazonka-mlCreate a value of " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility:,  - Undocumented member.,  - Undocumented member. amazonka-mlUndocumented member. amazonka-mlUndocumented member. amazonka-ml amazonka-ml(c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred"%&';ؗ amazonka-mlThe data specification of an Amazon Relational Database Service (Amazon RDS)  DataSource.See:  smart constructor. amazonka-mlA JSON string that represents the splitting and rearrangement processing to be applied to a  DataSource . If the DataRearrangement parameter is not provided, all of the input data is used to create the  Datasource.There are multiple parameters that control what data is used to create a datasource: percentBeginUse  percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include  percentBegin and  percentEnd, Amazon ML includes all of the data when creating the datasource. percentEndUse  percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include  percentBegin and  percentEnd, Amazon ML includes all of the data when creating the datasource. complementThe  complement parameter instructs Amazon ML to use the data that is not included in the range of  percentBegin to  percentEnd to create a datasource. The  complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for  percentBegin and  percentEnd, along with the  complement parameter.For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.Datasource for evaluation: 1{"splitting":{"percentBegin":0, "percentEnd":25}}Datasource for training: {"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}}strategyTo change how Amazon ML splits the data for a datasource, use the strategy parameter.The default value for the strategy parameter is  sequential, meaning that Amazon ML takes all of the data records between the  percentBegin and  percentEnd parameters for the datasource, in the order that the records appear in the input data.The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}}Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}}To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between  percentBegin and  percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources:Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}} amazonka-ml;A JSON string that represents the schema for an Amazon RDS  DataSource. The  DataSchema defines the structure of the observation data in the data file(s) referenced in the  DataSource.A  DataSchema" is not required if you specify a  DataSchemaUri Define your  DataSchema! as a series of key-value pairs.  attributes and excludedVariableNames have an array of key-value pairs for their value. Use the following format to define your  DataSchema.{ "version": "1.0",""recordAnnotationFieldName": "F1","recordWeightFieldName": "F2","targetFieldName": "F3","dataFormat": "CSV","dataFileContainsHeader": true,"attributes": [{ "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName": "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL" }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType": "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" } ],#"excludedVariableNames": [ "F6" ] } amazonka-mlThe Amazon S3 location of the  DataSchema. amazonka-mlDescribes the  DatabaseName and InstanceIdentifier of an Amazon RDS database. amazonka-mlThe query that is used to retrieve the observation data for the  DataSource. amazonka-mlThe AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon RDS database. amazonka-mlThe Amazon S3 location for staging Amazon RDS data. The data retrieved from Amazon RDS using SelectSqlQuery is stored in this location. amazonka-mlThe role (DataPipelineDefaultResourceRole) assumed by an Amazon Elastic Compute Cloud (Amazon EC2) instance to carry out the copy operation from Amazon RDS to an Amazon S3 task. For more information, see  https://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.htmlRole templates for data pipelines. amazonka-mlThe role (DataPipelineDefaultRole) assumed by AWS Data Pipeline service to monitor the progress of the copy task from Amazon RDS to Amazon S3. For more information, see  https://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.htmlRole templates for data pipelines. amazonka-mlThe subnet ID to be used to access a VPC-based RDS DB instance. This attribute is used by Data Pipeline to carry out the copy task from Amazon RDS to Amazon S3. amazonka-mlThe security group IDs to be used to access a VPC-based RDS DB instance. Ensure that there are appropriate ingress rules set up to allow access to the RDS DB instance. This attribute is used by Data Pipeline to carry out the copy operation from Amazon RDS to an Amazon S3 task. amazonka-mlCreate a value of " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility:,  - A JSON string that represents the splitting and rearrangement processing to be applied to a  DataSource . If the DataRearrangement parameter is not provided, all of the input data is used to create the  Datasource.There are multiple parameters that control what data is used to create a datasource: percentBeginUse  percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include  percentBegin and  percentEnd, Amazon ML includes all of the data when creating the datasource. percentEndUse  percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include  percentBegin and  percentEnd, Amazon ML includes all of the data when creating the datasource. complementThe  complement parameter instructs Amazon ML to use the data that is not included in the range of  percentBegin to  percentEnd to create a datasource. The  complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for  percentBegin and  percentEnd, along with the  complement parameter.For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.Datasource for evaluation: 1{"splitting":{"percentBegin":0, "percentEnd":25}}Datasource for training: {"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}}strategyTo change how Amazon ML splits the data for a datasource, use the strategy parameter.The default value for the strategy parameter is  sequential, meaning that Amazon ML takes all of the data records between the  percentBegin and  percentEnd parameters for the datasource, in the order that the records appear in the input data.The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}}Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}}To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between  percentBegin and  percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources:Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}, > - A JSON string that represents the schema for an Amazon RDS  DataSource. The  DataSchema defines the structure of the observation data in the data file(s) referenced in the  DataSource.A  DataSchema" is not required if you specify a  DataSchemaUri Define your  DataSchema! as a series of key-value pairs.  attributes and excludedVariableNames have an array of key-value pairs for their value. Use the following format to define your  DataSchema.{ "version": "1.0",""recordAnnotationFieldName": "F1","recordWeightFieldName": "F2","targetFieldName": "F3","dataFormat": "CSV","dataFileContainsHeader": true,"attributes": [{ "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName": "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL" }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType": "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" } ],#"excludedVariableNames": [ "F6" ] }, ! - The Amazon S3 location of the  DataSchema.,  - Describes the  DatabaseName and InstanceIdentifier of an Amazon RDS database.,  - The query that is used to retrieve the observation data for the  DataSource.,  - The AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon RDS database.,  - The Amazon S3 location for staging Amazon RDS data. The data retrieved from Amazon RDS using SelectSqlQuery is stored in this location.,  - The role (DataPipelineDefaultResourceRole) assumed by an Amazon Elastic Compute Cloud (Amazon EC2) instance to carry out the copy operation from Amazon RDS to an Amazon S3 task. For more information, see  https://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.htmlRole templates for data pipelines.,  - The role (DataPipelineDefaultRole) assumed by AWS Data Pipeline service to monitor the progress of the copy task from Amazon RDS to Amazon S3. For more information, see  https://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.htmlRole templates for data pipelines.,  - The subnet ID to be used to access a VPC-based RDS DB instance. This attribute is used by Data Pipeline to carry out the copy task from Amazon RDS to Amazon S3.,  - The security group IDs to be used to access a VPC-based RDS DB instance. Ensure that there are appropriate ingress rules set up to allow access to the RDS DB instance. This attribute is used by Data Pipeline to carry out the copy operation from Amazon RDS to an Amazon S3 task. amazonka-mlA JSON string that represents the splitting and rearrangement processing to be applied to a  DataSource . If the DataRearrangement parameter is not provided, all of the input data is used to create the  Datasource.There are multiple parameters that control what data is used to create a datasource: percentBeginUse  percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include  percentBegin and  percentEnd, Amazon ML includes all of the data when creating the datasource. percentEndUse  percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include  percentBegin and  percentEnd, Amazon ML includes all of the data when creating the datasource. complementThe  complement parameter instructs Amazon ML to use the data that is not included in the range of  percentBegin to  percentEnd to create a datasource. The  complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for  percentBegin and  percentEnd, along with the  complement parameter.For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.Datasource for evaluation: 1{"splitting":{"percentBegin":0, "percentEnd":25}}Datasource for training: {"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}}strategyTo change how Amazon ML splits the data for a datasource, use the strategy parameter.The default value for the strategy parameter is  sequential, meaning that Amazon ML takes all of the data records between the  percentBegin and  percentEnd parameters for the datasource, in the order that the records appear in the input data.The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}}Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}}To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between  percentBegin and  percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources:Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}} amazonka-ml;A JSON string that represents the schema for an Amazon RDS  DataSource. The  DataSchema defines the structure of the observation data in the data file(s) referenced in the  DataSource.A  DataSchema" is not required if you specify a  DataSchemaUri Define your  DataSchema! as a series of key-value pairs.  attributes and excludedVariableNames have an array of key-value pairs for their value. Use the following format to define your  DataSchema.{ "version": "1.0",""recordAnnotationFieldName": "F1","recordWeightFieldName": "F2","targetFieldName": "F3","dataFormat": "CSV","dataFileContainsHeader": true,"attributes": [{ "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName": "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL" }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType": "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" } ],#"excludedVariableNames": [ "F6" ] } amazonka-mlThe Amazon S3 location of the  DataSchema. amazonka-mlDescribes the  DatabaseName and InstanceIdentifier of an Amazon RDS database. amazonka-mlThe query that is used to retrieve the observation data for the  DataSource. amazonka-mlThe AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon RDS database. amazonka-mlThe Amazon S3 location for staging Amazon RDS data. The data retrieved from Amazon RDS using SelectSqlQuery is stored in this location. amazonka-mlThe role (DataPipelineDefaultResourceRole) assumed by an Amazon Elastic Compute Cloud (Amazon EC2) instance to carry out the copy operation from Amazon RDS to an Amazon S3 task. For more information, see  https://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.htmlRole templates for data pipelines. amazonka-mlThe role (DataPipelineDefaultRole) assumed by AWS Data Pipeline service to monitor the progress of the copy task from Amazon RDS to Amazon S3. For more information, see  https://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.htmlRole templates for data pipelines. amazonka-mlThe subnet ID to be used to access a VPC-based RDS DB instance. This attribute is used by Data Pipeline to carry out the copy task from Amazon RDS to Amazon S3. amazonka-mlThe security group IDs to be used to access a VPC-based RDS DB instance. Ensure that there are appropriate ingress rules set up to allow access to the RDS DB instance. This attribute is used by Data Pipeline to carry out the copy operation from Amazon RDS to an Amazon S3 task. amazonka-ml amazonka-ml amazonka-ml amazonka-ml amazonka-ml amazonka-ml amazonka-ml(c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred"%&';L  amazonka-ml7The datasource details that are specific to Amazon RDS.See:  smart constructor. amazonka-mlThe ID of the Data Pipeline instance that is used to carry to copy data from Amazon RDS to Amazon S3. You can use the ID to find details about the instance in the Data Pipeline console. amazonka-ml:The database details required to connect to an Amazon RDS. amazonka-mlThe role (DataPipelineDefaultResourceRole) assumed by an Amazon EC2 instance to carry out the copy task from Amazon RDS to Amazon S3. For more information, see  https://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.htmlRole templates for data pipelines. amazonka-mlThe SQL query that is supplied during CreateDataSourceFromRDS. Returns only if Verbose is true in GetDataSourceInput. amazonka-mlThe role (DataPipelineDefaultRole) assumed by the Data Pipeline service to monitor the progress of the copy task from Amazon RDS to Amazon S3. For more information, see  https://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.htmlRole templates for data pipelines. amazonka-mlCreate a value of " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility:,  - The ID of the Data Pipeline instance that is used to carry to copy data from Amazon RDS to Amazon S3. You can use the ID to find details about the instance in the Data Pipeline console., = - The database details required to connect to an Amazon RDS.,  - Undocumented member.,  - The role (DataPipelineDefaultResourceRole) assumed by an Amazon EC2 instance to carry out the copy task from Amazon RDS to Amazon S3. For more information, see  https://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.htmlRole templates for data pipelines.,  - The SQL query that is supplied during CreateDataSourceFromRDS. Returns only if Verbose is true in GetDataSourceInput.,  - The role (DataPipelineDefaultRole) assumed by the Data Pipeline service to monitor the progress of the copy task from Amazon RDS to Amazon S3. For more information, see  https://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.htmlRole templates for data pipelines. amazonka-mlThe ID of the Data Pipeline instance that is used to carry to copy data from Amazon RDS to Amazon S3. You can use the ID to find details about the instance in the Data Pipeline console. amazonka-ml:The database details required to connect to an Amazon RDS. amazonka-mlUndocumented member. amazonka-mlThe role (DataPipelineDefaultResourceRole) assumed by an Amazon EC2 instance to carry out the copy task from Amazon RDS to Amazon S3. For more information, see  https://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.htmlRole templates for data pipelines. amazonka-mlThe SQL query that is supplied during CreateDataSourceFromRDS. Returns only if Verbose is true in GetDataSourceInput. amazonka-mlThe role (DataPipelineDefaultRole) assumed by the Data Pipeline service to monitor the progress of the copy task from Amazon RDS to Amazon S3. For more information, see  https://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.htmlRole templates for data pipelines.(c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred";?, (c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred"%&';  amazonka-ml4Describes the real-time endpoint information for an MLModel.See:  smart constructor. amazonka-mlThe time that the request to create the real-time endpoint for the MLModel3 was received. The time is expressed in epoch time. amazonka-ml5The current status of the real-time endpoint for the MLModel5. This element can have one of the following values:NONE5 - Endpoint does not exist or was previously deleted.READY: - Endpoint is ready to be used for real-time predictions.UPDATING" - Updating/creating the endpoint. amazonka-mlThe URI that specifies where to send real-time prediction requests for the MLModel.Note: The application must wait until the real-time endpoint is ready before using this URI. amazonka-ml;The maximum processing rate for the real-time endpoint for MLModel,, measured in incoming requests per second. amazonka-mlCreate a value of " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility:,  - The time that the request to create the real-time endpoint for the MLModel3 was received. The time is expressed in epoch time., 8 - The current status of the real-time endpoint for the MLModel5. This element can have one of the following values:NONE5 - Endpoint does not exist or was previously deleted.READY: - Endpoint is ready to be used for real-time predictions.UPDATING" - Updating/creating the endpoint.,  - The URI that specifies where to send real-time prediction requests for the MLModel.Note: The application must wait until the real-time endpoint is ready before using this URI., > - The maximum processing rate for the real-time endpoint for MLModel,, measured in incoming requests per second. amazonka-mlThe time that the request to create the real-time endpoint for the MLModel3 was received. The time is expressed in epoch time. amazonka-ml5The current status of the real-time endpoint for the MLModel5. This element can have one of the following values:NONE5 - Endpoint does not exist or was previously deleted.READY: - Endpoint is ready to be used for real-time predictions.UPDATING" - Updating/creating the endpoint. amazonka-mlThe URI that specifies where to send real-time prediction requests for the MLModel.Note: The application must wait until the real-time endpoint is ready before using this URI. amazonka-ml;The maximum processing rate for the real-time endpoint for MLModel,, measured in incoming requests per second.  (c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred"%&';,# amazonka-mlRepresents the output of a  GetMLModel operation.The content consists of the detailed metadata and the current status of the MLModel.See:  smart constructor. amazonka-ml The algorithm used to train the MLModel(. The following algorithm is supported:SGD- -- Stochastic gradient descent. The goal of SGD7 is to minimize the gradient of the loss function. amazonka-mlThe time that the MLModel3 was created. The time is expressed in epoch time. amazonka-ml$The AWS user account from which the MLModel was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account. amazonka-mlThe current endpoint of the MLModel. amazonka-mlThe location of the data file or directory in Amazon Simple Storage Service (Amazon S3). amazonka-ml(The time of the most recent edit to the MLModel'. The time is expressed in epoch time. amazonka-mlThe ID assigned to the MLModel at creation. amazonka-mlIdentifies the MLModel2 category. The following are the available types: REGRESSION - Produces a numeric result. For example, "What price should a house be listed at?"BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?". MULTICLASS - Produces one of several possible results. For example, "Is this a HIGH-, LOW-, or MEDIUM-risk trade?". amazonka-ml=A description of the most recent details about accessing the MLModel. amazonka-ml+A user-supplied name or description of the MLModel. amazonka-ml(The time of the most recent edit to the ScoreThreshold'. The time is expressed in epoch time. amazonka-mlThe current status of an MLModel5. This element can have one of the following values:PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create an MLModel. INPROGRESS$ - The creation process is underway.FAILED - The request to create an MLModel7 didn't run to completion. The model isn't usable. COMPLETED/ - The creation process completed successfully.DELETED - The MLModel' is marked as deleted. It isn't usable. amazonka-mlThe ID of the training  DataSource. The  CreateMLModel operation uses the TrainingDataSourceId. amazonka-ml)A list of the training parameters in the MLModel7. The list is implemented as a map of key-value pairs.8The following is the current set of training parameters:sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.)The value is an integer that ranges from 100000 to  2147483648. The default value is 33554432. sgd.maxPasses - The number of times that the training process traverses the observations to build the MLModel0. The value is an integer that ranges from 1 to 10000. The default value is 10.sgd.shuffleType - Whether Amazon ML shuffles the training data. Shuffling the data improves a model's ability to find the optimal solution for a variety of data types. The valid values are auto and none. The default value is none.sgd.l1RegularizationAmount - The coefficient regularization L1 norm, which controls overfitting the data by penalizing large coefficients. This parameter tends to drive coefficients to zero, resulting in sparse feature set. If you use this parameter, start by specifying a small value, such as 1.0E-08.'The value is a double that ranges from 0 to  MAX_DOUBLE. The default is to not use L1 normalization. This parameter can't be used when L2, is specified. Use this parameter sparingly.sgd.l2RegularizationAmount - The coefficient regularization L2 norm, which controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as 1.0E-08.'The value is a double that ranges from 0 to  MAX_DOUBLE. The default is to not use L2 normalization. This parameter can't be used when L1, is specified. Use this parameter sparingly. amazonka-mlCreate a value of " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility:, # - The algorithm used to train the MLModel(. The following algorithm is supported:SGD- -- Stochastic gradient descent. The goal of SGD7 is to minimize the gradient of the loss function.,  - Undocumented member.,  - The time that the MLModel3 was created. The time is expressed in epoch time., ' - The AWS user account from which the MLModel was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.,  - The current endpoint of the MLModel.,  - Undocumented member.,  - The location of the data file or directory in Amazon Simple Storage Service (Amazon S3)., + - The time of the most recent edit to the MLModel'. The time is expressed in epoch time.,  - The ID assigned to the MLModel at creation.,  - Identifies the MLModel2 category. The following are the available types: REGRESSION - Produces a numeric result. For example, "What price should a house be listed at?"BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?". MULTICLASS - Produces one of several possible results. For example, "Is this a HIGH-, LOW-, or MEDIUM-risk trade?".,  - A description of the most recent details about accessing the MLModel., . - A user-supplied name or description of the MLModel.,  - Undocumented member., + - The time of the most recent edit to the ScoreThreshold'. The time is expressed in epoch time.,  - Undocumented member.,  - Undocumented member.,  - The current status of an MLModel5. This element can have one of the following values:PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create an MLModel. INPROGRESS$ - The creation process is underway.FAILED - The request to create an MLModel7 didn't run to completion. The model isn't usable. COMPLETED/ - The creation process completed successfully.DELETED - The MLModel' is marked as deleted. It isn't usable.,  - The ID of the training  DataSource. The  CreateMLModel operation uses the TrainingDataSourceId., , - A list of the training parameters in the MLModel7. The list is implemented as a map of key-value pairs.8The following is the current set of training parameters:sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.)The value is an integer that ranges from 100000 to  2147483648. The default value is 33554432. sgd.maxPasses - The number of times that the training process traverses the observations to build the MLModel0. The value is an integer that ranges from 1 to 10000. The default value is 10.sgd.shuffleType - Whether Amazon ML shuffles the training data. Shuffling the data improves a model's ability to find the optimal solution for a variety of data types. The valid values are auto and none. The default value is none.sgd.l1RegularizationAmount - The coefficient regularization L1 norm, which controls overfitting the data by penalizing large coefficients. This parameter tends to drive coefficients to zero, resulting in sparse feature set. If you use this parameter, start by specifying a small value, such as 1.0E-08.'The value is a double that ranges from 0 to  MAX_DOUBLE. The default is to not use L1 normalization. This parameter can't be used when L2, is specified. Use this parameter sparingly.sgd.l2RegularizationAmount - The coefficient regularization L2 norm, which controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as 1.0E-08.'The value is a double that ranges from 0 to  MAX_DOUBLE. The default is to not use L2 normalization. This parameter can't be used when L1, is specified. Use this parameter sparingly. amazonka-ml The algorithm used to train the MLModel(. The following algorithm is supported:SGD- -- Stochastic gradient descent. The goal of SGD7 is to minimize the gradient of the loss function. amazonka-mlUndocumented member. amazonka-mlThe time that the MLModel3 was created. The time is expressed in epoch time. amazonka-ml$The AWS user account from which the MLModel was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account. amazonka-mlThe current endpoint of the MLModel. amazonka-mlUndocumented member. amazonka-mlThe location of the data file or directory in Amazon Simple Storage Service (Amazon S3). amazonka-ml(The time of the most recent edit to the MLModel'. The time is expressed in epoch time. amazonka-mlThe ID assigned to the MLModel at creation. amazonka-mlIdentifies the MLModel2 category. The following are the available types: REGRESSION - Produces a numeric result. For example, "What price should a house be listed at?"BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?". MULTICLASS - Produces one of several possible results. For example, "Is this a HIGH-, LOW-, or MEDIUM-risk trade?". amazonka-ml=A description of the most recent details about accessing the MLModel. amazonka-ml+A user-supplied name or description of the MLModel. amazonka-mlUndocumented member. amazonka-ml(The time of the most recent edit to the ScoreThreshold'. The time is expressed in epoch time. amazonka-mlUndocumented member. amazonka-mlUndocumented member. amazonka-mlThe current status of an MLModel5. This element can have one of the following values:PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create an MLModel. INPROGRESS$ - The creation process is underway.FAILED - The request to create an MLModel7 didn't run to completion. The model isn't usable. COMPLETED/ - The creation process completed successfully.DELETED - The MLModel' is marked as deleted. It isn't usable. amazonka-mlThe ID of the training  DataSource. The  CreateMLModel operation uses the TrainingDataSourceId. amazonka-ml)A list of the training parameters in the MLModel7. The list is implemented as a map of key-value pairs.8The following is the current set of training parameters:sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.)The value is an integer that ranges from 100000 to  2147483648. The default value is 33554432. sgd.maxPasses - The number of times that the training process traverses the observations to build the MLModel0. The value is an integer that ranges from 1 to 10000. The default value is 10.sgd.shuffleType - Whether Amazon ML shuffles the training data. Shuffling the data improves a model's ability to find the optimal solution for a variety of data types. The valid values are auto and none. The default value is none.sgd.l1RegularizationAmount - The coefficient regularization L1 norm, which controls overfitting the data by penalizing large coefficients. This parameter tends to drive coefficients to zero, resulting in sparse feature set. If you use this parameter, start by specifying a small value, such as 1.0E-08.'The value is a double that ranges from 0 to  MAX_DOUBLE. The default is to not use L1 normalization. This parameter can't be used when L2, is specified. Use this parameter sparingly.sgd.l2RegularizationAmount - The coefficient regularization L2 norm, which controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as 1.0E-08.'The value is a double that ranges from 0 to  MAX_DOUBLE. The default is to not use L2 normalization. This parameter can't be used when L1, is specified. Use this parameter sparingly.))(c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred"%&';1 amazonka-mlDescribes the database details required to connect to an Amazon Redshift database.See:  smart constructor. amazonka-mlCreate a value of " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility:,  - Undocumented member.,  - Undocumented member. amazonka-mlUndocumented member. amazonka-mlUndocumented member. amazonka-ml amazonka-ml(c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred"%&';4 amazonka-mlDescribes the database credentials for connecting to a database on an Amazon Redshift cluster.See:  smart constructor. amazonka-mlCreate a value of " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility:,  - Undocumented member.,  - Undocumented member. amazonka-mlUndocumented member. amazonka-mlUndocumented member. amazonka-ml amazonka-ml(c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred"%&';{= amazonka-ml7Describes the data specification of an Amazon Redshift  DataSource.See:  smart constructor. amazonka-mlA JSON string that represents the splitting and rearrangement processing to be applied to a  DataSource . If the DataRearrangement parameter is not provided, all of the input data is used to create the  Datasource.There are multiple parameters that control what data is used to create a datasource: percentBeginUse  percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include  percentBegin and  percentEnd, Amazon ML includes all of the data when creating the datasource. percentEndUse  percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include  percentBegin and  percentEnd, Amazon ML includes all of the data when creating the datasource. complementThe  complement parameter instructs Amazon ML to use the data that is not included in the range of  percentBegin to  percentEnd to create a datasource. The  complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for  percentBegin and  percentEnd, along with the  complement parameter.For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.Datasource for evaluation: 1{"splitting":{"percentBegin":0, "percentEnd":25}}Datasource for training: {"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}}strategyTo change how Amazon ML splits the data for a datasource, use the strategy parameter.The default value for the strategy parameter is  sequential, meaning that Amazon ML takes all of the data records between the  percentBegin and  percentEnd parameters for the datasource, in the order that the records appear in the input data.The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}}Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}}To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between  percentBegin and  percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources:Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}} amazonka-mlA JSON string that represents the schema for an Amazon Redshift  DataSource. The  DataSchema defines the structure of the observation data in the data file(s) referenced in the  DataSource.A  DataSchema" is not required if you specify a  DataSchemaUri. Define your  DataSchema! as a series of key-value pairs.  attributes and excludedVariableNames have an array of key-value pairs for their value. Use the following format to define your  DataSchema.{ "version": "1.0",""recordAnnotationFieldName": "F1","recordWeightFieldName": "F2","targetFieldName": "F3","dataFormat": "CSV","dataFileContainsHeader": true,"attributes": [{ "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName": "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL" }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType": "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" } ],#"excludedVariableNames": [ "F6" ] } amazonka-ml5Describes the schema location for an Amazon Redshift  DataSource. amazonka-mlDescribes the  DatabaseName and ClusterIdentifier for an Amazon Redshift  DataSource. amazonka-mlDescribes the SQL Query to execute on an Amazon Redshift database for an Amazon Redshift  DataSource. amazonka-mlDescribes AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon Redshift database. amazonka-mlDescribes an Amazon S3 location to store the result set of the SelectSqlQuery query. amazonka-mlCreate a value of " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility:,  - A JSON string that represents the splitting and rearrangement processing to be applied to a  DataSource . If the DataRearrangement parameter is not provided, all of the input data is used to create the  Datasource.There are multiple parameters that control what data is used to create a datasource: percentBeginUse  percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include  percentBegin and  percentEnd, Amazon ML includes all of the data when creating the datasource. percentEndUse  percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include  percentBegin and  percentEnd, Amazon ML includes all of the data when creating the datasource. complementThe  complement parameter instructs Amazon ML to use the data that is not included in the range of  percentBegin to  percentEnd to create a datasource. The  complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for  percentBegin and  percentEnd, along with the  complement parameter.For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.Datasource for evaluation: 1{"splitting":{"percentBegin":0, "percentEnd":25}}Datasource for training: {"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}}strategyTo change how Amazon ML splits the data for a datasource, use the strategy parameter.The default value for the strategy parameter is  sequential, meaning that Amazon ML takes all of the data records between the  percentBegin and  percentEnd parameters for the datasource, in the order that the records appear in the input data.The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}}Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}}To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between  percentBegin and  percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources:Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}},  - A JSON string that represents the schema for an Amazon Redshift  DataSource. The  DataSchema defines the structure of the observation data in the data file(s) referenced in the  DataSource.A  DataSchema" is not required if you specify a  DataSchemaUri. Define your  DataSchema! as a series of key-value pairs.  attributes and excludedVariableNames have an array of key-value pairs for their value. Use the following format to define your  DataSchema.{ "version": "1.0",""recordAnnotationFieldName": "F1","recordWeightFieldName": "F2","targetFieldName": "F3","dataFormat": "CSV","dataFileContainsHeader": true,"attributes": [{ "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName": "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL" }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType": "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" } ],#"excludedVariableNames": [ "F6" ] }, 8 - Describes the schema location for an Amazon Redshift  DataSource.,  - Describes the  DatabaseName and ClusterIdentifier for an Amazon Redshift  DataSource.,  - Describes the SQL Query to execute on an Amazon Redshift database for an Amazon Redshift  DataSource.,  - Describes AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon Redshift database.,  - Describes an Amazon S3 location to store the result set of the SelectSqlQuery query. amazonka-mlA JSON string that represents the splitting and rearrangement processing to be applied to a  DataSource . If the DataRearrangement parameter is not provided, all of the input data is used to create the  Datasource.There are multiple parameters that control what data is used to create a datasource: percentBeginUse  percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include  percentBegin and  percentEnd, Amazon ML includes all of the data when creating the datasource. percentEndUse  percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include  percentBegin and  percentEnd, Amazon ML includes all of the data when creating the datasource. complementThe  complement parameter instructs Amazon ML to use the data that is not included in the range of  percentBegin to  percentEnd to create a datasource. The  complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for  percentBegin and  percentEnd, along with the  complement parameter.For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.Datasource for evaluation: 1{"splitting":{"percentBegin":0, "percentEnd":25}}Datasource for training: {"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}}strategyTo change how Amazon ML splits the data for a datasource, use the strategy parameter.The default value for the strategy parameter is  sequential, meaning that Amazon ML takes all of the data records between the  percentBegin and  percentEnd parameters for the datasource, in the order that the records appear in the input data.The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}}Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}}To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between  percentBegin and  percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources:Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}} amazonka-mlA JSON string that represents the schema for an Amazon Redshift  DataSource. The  DataSchema defines the structure of the observation data in the data file(s) referenced in the  DataSource.A  DataSchema" is not required if you specify a  DataSchemaUri. Define your  DataSchema! as a series of key-value pairs.  attributes and excludedVariableNames have an array of key-value pairs for their value. Use the following format to define your  DataSchema.{ "version": "1.0",""recordAnnotationFieldName": "F1","recordWeightFieldName": "F2","targetFieldName": "F3","dataFormat": "CSV","dataFileContainsHeader": true,"attributes": [{ "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName": "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL" }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType": "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" } ],#"excludedVariableNames": [ "F6" ] } amazonka-ml5Describes the schema location for an Amazon Redshift  DataSource. amazonka-mlDescribes the  DatabaseName and ClusterIdentifier for an Amazon Redshift  DataSource. amazonka-mlDescribes the SQL Query to execute on an Amazon Redshift database for an Amazon Redshift  DataSource. amazonka-mlDescribes AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon Redshift database. amazonka-mlDescribes an Amazon S3 location to store the result set of the SelectSqlQuery query. amazonka-ml amazonka-ml amazonka-ml amazonka-ml(c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred"%&';z amazonka-mlDescribes the  DataSource% details specific to Amazon Redshift.See:  smart constructor. amazonka-mlThe SQL query that is specified during CreateDataSourceFromRedshift. Returns only if Verbose is true in GetDataSourceInput. amazonka-mlCreate a value of " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility:,  - Undocumented member.,  - Undocumented member.,  - The SQL query that is specified during CreateDataSourceFromRedshift. Returns only if Verbose is true in GetDataSourceInput. amazonka-mlUndocumented member. amazonka-mlUndocumented member. amazonka-mlThe SQL query that is specified during CreateDataSourceFromRedshift. Returns only if Verbose is true in GetDataSourceInput.  (c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred"%&';  amazonka-mlRepresents the output of the  GetDataSource operation.The content consists of the detailed metadata and data file information and the current status of the  DataSource.See:  smart constructor. amazonka-mlThe parameter is true? if statistics need to be generated from the observation data. amazonka-mlThe time that the  DataSource3 was created. The time is expressed in epoch time. amazonka-ml$The AWS user account from which the  DataSource was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account. amazonka-mlThe location and name of the data in Amazon Simple Storage Service (Amazon S3) that is used by a  DataSource. amazonka-mlA JSON string that represents the splitting and rearrangement requirement used when this  DataSource was created. amazonka-mlThe total number of observations contained in the data files that the  DataSource references. amazonka-mlThe ID that is assigned to the  DataSource during creation. amazonka-ml(The time of the most recent edit to the BatchPrediction'. The time is expressed in epoch time. amazonka-ml=A description of the most recent details about creating the  DataSource. amazonka-ml+A user-supplied name or description of the  DataSource. amazonka-ml+The number of data files referenced by the  DataSource. amazonka-mlThe current status of the  DataSource5. This element can have one of the following values:PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create a  DataSource..INPROGRESS - The creation process is underway.!FAILED - The request to create a  DataSource2 did not run to completion. It is not usable.8COMPLETED - The creation process completed successfully.DELETED - The  DataSource( is marked as deleted. It is not usable. amazonka-mlCreate a value of " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility:,  - The parameter is true? if statistics need to be generated from the observation data.,  - Undocumented member.,  - The time that the  DataSource3 was created. The time is expressed in epoch time., ' - The AWS user account from which the  DataSource was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.,  - The location and name of the data in Amazon Simple Storage Service (Amazon S3) that is used by a  DataSource.,  - A JSON string that represents the splitting and rearrangement requirement used when this  DataSource was created.,  - The total number of observations contained in the data files that the  DataSource references., " - The ID that is assigned to the  DataSource during creation.,  - Undocumented member., + - The time of the most recent edit to the BatchPrediction'. The time is expressed in epoch time.,  - A description of the most recent details about creating the  DataSource., . - A user-supplied name or description of the  DataSource., . - The number of data files referenced by the  DataSource.,  - Undocumented member.,  - Undocumented member.,  - Undocumented member.,  - Undocumented member.,  - The current status of the  DataSource5. This element can have one of the following values:PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create a  DataSource..INPROGRESS - The creation process is underway.!FAILED - The request to create a  DataSource2 did not run to completion. It is not usable.8COMPLETED - The creation process completed successfully.DELETED - The  DataSource( is marked as deleted. It is not usable. amazonka-mlThe parameter is true? if statistics need to be generated from the observation data. amazonka-mlUndocumented member. amazonka-mlThe time that the  DataSource3 was created. The time is expressed in epoch time. amazonka-ml$The AWS user account from which the  DataSource was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account. amazonka-mlThe location and name of the data in Amazon Simple Storage Service (Amazon S3) that is used by a  DataSource. amazonka-mlA JSON string that represents the splitting and rearrangement requirement used when this  DataSource was created. amazonka-mlThe total number of observations contained in the data files that the  DataSource references. amazonka-mlThe ID that is assigned to the  DataSource during creation. amazonka-mlUndocumented member. amazonka-ml(The time of the most recent edit to the BatchPrediction'. The time is expressed in epoch time. amazonka-ml=A description of the most recent details about creating the  DataSource. amazonka-ml+A user-supplied name or description of the  DataSource. amazonka-ml+The number of data files referenced by the  DataSource. amazonka-mlUndocumented member. amazonka-mlUndocumented member. amazonka-mlUndocumented member. amazonka-mlUndocumented member. amazonka-mlThe current status of the  DataSource5. This element can have one of the following values:PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create a  DataSource..INPROGRESS - The creation process is underway.!FAILED - The request to create a  DataSource2 did not run to completion. It is not usable.8COMPLETED - The creation process completed successfully.DELETED - The  DataSource( is marked as deleted. It is not usable.''(c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred"%&';ނ  amazonka-ml&Describes the data specification of a  DataSource.See:  smart constructor. amazonka-mlA JSON string that represents the splitting and rearrangement processing to be applied to a  DataSource . If the DataRearrangement parameter is not provided, all of the input data is used to create the  Datasource.There are multiple parameters that control what data is used to create a datasource: percentBeginUse  percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include  percentBegin and  percentEnd, Amazon ML includes all of the data when creating the datasource. percentEndUse  percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include  percentBegin and  percentEnd, Amazon ML includes all of the data when creating the datasource. complementThe  complement parameter instructs Amazon ML to use the data that is not included in the range of  percentBegin to  percentEnd to create a datasource. The  complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for  percentBegin and  percentEnd, along with the  complement parameter.For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.Datasource for evaluation: 1{"splitting":{"percentBegin":0, "percentEnd":25}}Datasource for training: {"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}}strategyTo change how Amazon ML splits the data for a datasource, use the strategy parameter.The default value for the strategy parameter is  sequential, meaning that Amazon ML takes all of the data records between the  percentBegin and  percentEnd parameters for the datasource, in the order that the records appear in the input data.The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}}Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}}To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between  percentBegin and  percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources:Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}} amazonka-ml:A JSON string that represents the schema for an Amazon S3  DataSource. The  DataSchema defines the structure of the observation data in the data file(s) referenced in the  DataSource.You must provide either the  DataSchema or the DataSchemaLocationS3. Define your  DataSchema! as a series of key-value pairs.  attributes and excludedVariableNames have an array of key-value pairs for their value. Use the following format to define your  DataSchema.{ "version": "1.0",""recordAnnotationFieldName": "F1","recordWeightFieldName": "F2","targetFieldName": "F3","dataFormat": "CSV","dataFileContainsHeader": true,"attributes": [{ "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName": "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL" }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType": "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" } ],#"excludedVariableNames": [ "F6" ] } amazonka-mlDescribes the schema location in Amazon S3. You must provide either the  DataSchema or the DataSchemaLocationS3. amazonka-ml+The location of the data file(s) used by a  DataSource. The URI specifies a data file or an Amazon Simple Storage Service (Amazon S3) directory or bucket containing data files. amazonka-mlCreate a value of " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility:,  - A JSON string that represents the splitting and rearrangement processing to be applied to a  DataSource . If the DataRearrangement parameter is not provided, all of the input data is used to create the  Datasource.There are multiple parameters that control what data is used to create a datasource: percentBeginUse  percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include  percentBegin and  percentEnd, Amazon ML includes all of the data when creating the datasource. percentEndUse  percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include  percentBegin and  percentEnd, Amazon ML includes all of the data when creating the datasource. complementThe  complement parameter instructs Amazon ML to use the data that is not included in the range of  percentBegin to  percentEnd to create a datasource. The  complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for  percentBegin and  percentEnd, along with the  complement parameter.For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.Datasource for evaluation: 1{"splitting":{"percentBegin":0, "percentEnd":25}}Datasource for training: {"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}}strategyTo change how Amazon ML splits the data for a datasource, use the strategy parameter.The default value for the strategy parameter is  sequential, meaning that Amazon ML takes all of the data records between the  percentBegin and  percentEnd parameters for the datasource, in the order that the records appear in the input data.The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}}Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}}To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between  percentBegin and  percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources:Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}, = - A JSON string that represents the schema for an Amazon S3  DataSource. The  DataSchema defines the structure of the observation data in the data file(s) referenced in the  DataSource.You must provide either the  DataSchema or the DataSchemaLocationS3. Define your  DataSchema! as a series of key-value pairs.  attributes and excludedVariableNames have an array of key-value pairs for their value. Use the following format to define your  DataSchema.{ "version": "1.0",""recordAnnotationFieldName": "F1","recordWeightFieldName": "F2","targetFieldName": "F3","dataFormat": "CSV","dataFileContainsHeader": true,"attributes": [{ "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName": "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL" }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType": "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" } ],#"excludedVariableNames": [ "F6" ] },  - Describes the schema location in Amazon S3. You must provide either the  DataSchema or the DataSchemaLocationS3., . - The location of the data file(s) used by a  DataSource. The URI specifies a data file or an Amazon Simple Storage Service (Amazon S3) directory or bucket containing data files. amazonka-mlA JSON string that represents the splitting and rearrangement processing to be applied to a  DataSource . If the DataRearrangement parameter is not provided, all of the input data is used to create the  Datasource.There are multiple parameters that control what data is used to create a datasource: percentBeginUse  percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include  percentBegin and  percentEnd, Amazon ML includes all of the data when creating the datasource. percentEndUse  percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include  percentBegin and  percentEnd, Amazon ML includes all of the data when creating the datasource. complementThe  complement parameter instructs Amazon ML to use the data that is not included in the range of  percentBegin to  percentEnd to create a datasource. The  complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for  percentBegin and  percentEnd, along with the  complement parameter.For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.Datasource for evaluation: 1{"splitting":{"percentBegin":0, "percentEnd":25}}Datasource for training: {"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}}strategyTo change how Amazon ML splits the data for a datasource, use the strategy parameter.The default value for the strategy parameter is  sequential, meaning that Amazon ML takes all of the data records between the  percentBegin and  percentEnd parameters for the datasource, in the order that the records appear in the input data.The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}}Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}}To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between  percentBegin and  percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources:Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}} amazonka-ml:A JSON string that represents the schema for an Amazon S3  DataSource. The  DataSchema defines the structure of the observation data in the data file(s) referenced in the  DataSource.You must provide either the  DataSchema or the DataSchemaLocationS3. Define your  DataSchema! as a series of key-value pairs.  attributes and excludedVariableNames have an array of key-value pairs for their value. Use the following format to define your  DataSchema.{ "version": "1.0",""recordAnnotationFieldName": "F1","recordWeightFieldName": "F2","targetFieldName": "F3","dataFormat": "CSV","dataFileContainsHeader": true,"attributes": [{ "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName": "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL" }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType": "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" } ],#"excludedVariableNames": [ "F6" ] } amazonka-mlDescribes the schema location in Amazon S3. You must provide either the  DataSchema or the DataSchemaLocationS3. amazonka-ml+The location of the data file(s) used by a  DataSource. The URI specifies a data file or an Amazon Simple Storage Service (Amazon S3) directory or bucket containing data files. amazonka-ml  (c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred";?H amazonka-mlThe sort order specified in a listing condition. Possible values include the following:asc9 - Present the information in ascending order (from A-Z).dsc: - Present the information in descending order (from Z-A).(c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred"%&'; amazonka-mlA custom key-value pair associated with an ML object, such as an ML model.See:  smart constructor. amazonka-mlA unique identifier for the tag. Valid characters include Unicode letters, digits, white space, _, ., /, =, +, -, %, and @. amazonka-mlAn optional string, typically used to describe or define the tag. Valid characters include Unicode letters, digits, white space, _, ., /, =, +, -, %, and @. amazonka-mlCreate a value of " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility:,  - A unique identifier for the tag. Valid characters include Unicode letters, digits, white space, _, ., /, =, +, -, %, and @.,  - An optional string, typically used to describe or define the tag. Valid characters include Unicode letters, digits, white space, _, ., /, =, +, -, %, and @. amazonka-mlA unique identifier for the tag. Valid characters include Unicode letters, digits, white space, _, ., /, =, +, -, %, and @. amazonka-mlAn optional string, typically used to describe or define the tag. Valid characters include Unicode letters, digits, white space, _, ., /, =, +, -, %, and @.(c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred";? (c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred"%  amazonka-ml API version  2014-12-122 of the Amazon Machine Learning SDK configuration. amazonka-mlA second request to use or change an object was not allowed. This can result from retrying a request using a parameter that was not present in the original request. amazonka-mlAn error on the server occurred when trying to process a request. amazonka-mlAn error on the client occurred. Typically, the cause is an invalid input value. amazonka-ml&Prism for InvalidTagException' errors. amazonka-mlThe subscriber exceeded the maximum number of operations. This exception can occur when listing objects such as  DataSource. amazonka-mlThe exception is thrown when a predict request is made to an unmounted MLModel. amazonka-ml'A specified resource cannot be located. amazonka-ml,Prism for TagLimitExceededException' errors.! 5=<;:9867QUTRSiponmljk! ! 5=<;:9867=<;:98QUTRSUTiponmljkponml(c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred "%&';G  amazonka-mlSee:  smart constructor. amazonka-ml The response's http status code. amazonka-mlSee:  smart constructor. amazonka-mlA unique identifier of the MLModel. amazonka-mlCreate a value of " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility:,  - A unique identifier of the MLModel.,  - Undocumented member.,  - Undocumented member. amazonka-mlA unique identifier of the MLModel. amazonka-mlUndocumented member. amazonka-mlUndocumented member. amazonka-mlCreate a value of " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility:,  - Undocumented member., # - The response's http status code. amazonka-mlUndocumented member. amazonka-ml The response's http status code. amazonka-ml amazonka-ml amazonka-ml(c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred "%&';D3 amazonka-mlRepresents the output of a  GetMLModel7 operation, and provides detailed information about a MLModel.See:  smart constructor. amazonka-mlThe approximate CPU time in milliseconds that Amazon Machine Learning spent processing the MLModel3, normalized and scaled on computation resources.  ComputeTime is only available if the MLModel is in the  COMPLETED state. amazonka-mlThe time that the MLModel3 was created. The time is expressed in epoch time. amazonka-ml$The AWS user account from which the MLModel was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account. amazonka-mlThe current endpoint of the MLModel amazonka-ml7The epoch time when Amazon Machine Learning marked the MLModel as  COMPLETED or FAILED.  FinishedAt is only available when the MLModel is in the  COMPLETED or FAILED state. amazonka-mlThe location of the data file or directory in Amazon Simple Storage Service (Amazon S3). amazonka-ml(The time of the most recent edit to the MLModel'. The time is expressed in epoch time. amazonka-ml-A link to the file that contains logs of the  CreateMLModel operation. amazonka-ml%The MLModel ID, which is same as the  MLModelId in the request. amazonka-mlIdentifies the MLModel2 category. The following are the available types:REGRESSION -- Produces a numeric result. For example, "What price should a house be listed at?"BINARY -- Produces one of two possible results. For example, "Is this an e-commerce website?"MULTICLASS -- Produces one of several possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?" amazonka-ml=A description of the most recent details about accessing the MLModel. amazonka-ml+A user-supplied name or description of the MLModel. amazonka-ml$The recipe to use when training the MLModel. The Recipe provides detailed information about the observation data to use during training, and manipulations to perform on the observation data during training.Note:: This parameter is provided as part of the verbose format. amazonka-ml;The schema used by all of the data files referenced by the  DataSource.Note:: This parameter is provided as part of the verbose format. amazonka-ml7The scoring threshold is used in binary classification MLModel models. It marks the boundary between a positive prediction and a negative prediction.Output values greater than or equal to the threshold receive a positive result from the MLModel, such as true. Output values less than the threshold receive a negative response from the MLModel, such as false. amazonka-ml(The time of the most recent edit to the ScoreThreshold'. The time is expressed in epoch time. amazonka-ml7The epoch time when Amazon Machine Learning marked the MLModel as  INPROGRESS.  StartedAt isn't available if the MLModel is in the PENDING state. amazonka-mlThe current status of the MLModel5. This element can have one of the following values:PENDING - Amazon Machine Learning (Amazon ML) submitted a request to describe a MLModel. INPROGRESS - The request is processing.FAILED - The request did not run to completion. The ML model isn't usable. COMPLETED& - The request completed successfully.DELETED - The MLModel' is marked as deleted. It isn't usable. amazonka-mlThe ID of the training  DataSource. amazonka-ml)A list of the training parameters in the MLModel7. The list is implemented as a map of key-value pairs.8The following is the current set of training parameters:sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.)The value is an integer that ranges from 100000 to  2147483648. The default value is 33554432. sgd.maxPasses - The number of times that the training process traverses the observations to build the MLModel0. The value is an integer that ranges from 1 to 10000. The default value is 10.sgd.shuffleType - Whether Amazon ML shuffles the training data. Shuffling data improves a model's ability to find the optimal solution for a variety of data types. The valid values are auto and none. The default value is none8. We strongly recommend that you shuffle your data.sgd.l1RegularizationAmount - The coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, start by specifying a small value, such as 1.0E-08.'The value is a double that ranges from 0 to  MAX_DOUBLE. The default is to not use L1 normalization. This parameter can't be used when L2, is specified. Use this parameter sparingly.sgd.l2RegularizationAmount - The coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as 1.0E-08.'The value is a double that ranges from 0 to  MAX_DOUBLE. The default is to not use L2 normalization. This parameter can't be used when L1, is specified. Use this parameter sparingly. amazonka-ml The response's http status code. amazonka-mlSee:  smart constructor. amazonka-mlSpecifies whether the  GetMLModel operation should return Recipe. If true, Recipe is returned. If false, Recipe is not returned. amazonka-mlThe ID assigned to the MLModel at creation. amazonka-mlCreate a value of " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility:,  - Specifies whether the  GetMLModel operation should return Recipe. If true, Recipe is returned. If false, Recipe is not returned.,  - The ID assigned to the MLModel at creation. amazonka-mlSpecifies whether the  GetMLModel operation should return Recipe. If true, Recipe is returned. If false, Recipe is not returned. amazonka-mlThe ID assigned to the MLModel at creation. amazonka-mlCreate a value of " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility:,  - The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the MLModel3, normalized and scaled on computation resources.  ComputeTime is only available if the MLModel is in the  COMPLETED state.,  - The time that the MLModel3 was created. The time is expressed in epoch time., ' - The AWS user account from which the MLModel was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.,  - The current endpoint of the MLModel, : - The epoch time when Amazon Machine Learning marked the MLModel as  COMPLETED or FAILED.  FinishedAt is only available when the MLModel is in the  COMPLETED or FAILED state.,  - The location of the data file or directory in Amazon Simple Storage Service (Amazon S3)., + - The time of the most recent edit to the MLModel'. The time is expressed in epoch time., 0 - A link to the file that contains logs of the  CreateMLModel operation., ( - The MLModel ID, which is same as the  MLModelId in the request.,  - Identifies the MLModel2 category. The following are the available types:REGRESSION -- Produces a numeric result. For example, "What price should a house be listed at?"BINARY -- Produces one of two possible results. For example, "Is this an e-commerce website?"MULTICLASS -- Produces one of several possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?",  - A description of the most recent details about accessing the MLModel., . - A user-supplied name or description of the MLModel., ' - The recipe to use when training the MLModel. The Recipe provides detailed information about the observation data to use during training, and manipulations to perform on the observation data during training.Note:: This parameter is provided as part of the verbose format., > - The schema used by all of the data files referenced by the  DataSource.Note:: This parameter is provided as part of the verbose format., : - The scoring threshold is used in binary classification MLModel models. It marks the boundary between a positive prediction and a negative prediction.Output values greater than or equal to the threshold receive a positive result from the MLModel, such as true. Output values less than the threshold receive a negative response from the MLModel, such as false., + - The time of the most recent edit to the ScoreThreshold'. The time is expressed in epoch time.,  - Undocumented member., : - The epoch time when Amazon Machine Learning marked the MLModel as  INPROGRESS.  StartedAt isn't available if the MLModel is in the PENDING state.,  - The current status of the MLModel5. This element can have one of the following values:PENDING - Amazon Machine Learning (Amazon ML) submitted a request to describe a MLModel. INPROGRESS - The request is processing.FAILED - The request did not run to completion. The ML model isn't usable. COMPLETED& - The request completed successfully.DELETED - The MLModel' is marked as deleted. It isn't usable.,  - The ID of the training  DataSource., , - A list of the training parameters in the MLModel7. The list is implemented as a map of key-value pairs.8The following is the current set of training parameters:sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.)The value is an integer that ranges from 100000 to  2147483648. The default value is 33554432. sgd.maxPasses - The number of times that the training process traverses the observations to build the MLModel0. The value is an integer that ranges from 1 to 10000. The default value is 10.sgd.shuffleType - Whether Amazon ML shuffles the training data. Shuffling data improves a model's ability to find the optimal solution for a variety of data types. The valid values are auto and none. The default value is none8. We strongly recommend that you shuffle your data.sgd.l1RegularizationAmount - The coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, start by specifying a small value, such as 1.0E-08.'The value is a double that ranges from 0 to  MAX_DOUBLE. The default is to not use L1 normalization. This parameter can't be used when L2, is specified. Use this parameter sparingly.sgd.l2RegularizationAmount - The coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as 1.0E-08.'The value is a double that ranges from 0 to  MAX_DOUBLE. The default is to not use L2 normalization. This parameter can't be used when L1, is specified. Use this parameter sparingly., # - The response's http status code. amazonka-mlThe approximate CPU time in milliseconds that Amazon Machine Learning spent processing the MLModel3, normalized and scaled on computation resources.  ComputeTime is only available if the MLModel is in the  COMPLETED state. amazonka-mlThe time that the MLModel3 was created. The time is expressed in epoch time. amazonka-ml$The AWS user account from which the MLModel was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account. amazonka-mlThe current endpoint of the MLModel amazonka-ml7The epoch time when Amazon Machine Learning marked the MLModel as  COMPLETED or FAILED.  FinishedAt is only available when the MLModel is in the  COMPLETED or FAILED state. amazonka-mlThe location of the data file or directory in Amazon Simple Storage Service (Amazon S3). amazonka-ml(The time of the most recent edit to the MLModel'. The time is expressed in epoch time. amazonka-ml-A link to the file that contains logs of the  CreateMLModel operation. amazonka-ml%The MLModel ID, which is same as the  MLModelId in the request. amazonka-mlIdentifies the MLModel2 category. The following are the available types:REGRESSION -- Produces a numeric result. For example, "What price should a house be listed at?"BINARY -- Produces one of two possible results. For example, "Is this an e-commerce website?"MULTICLASS -- Produces one of several possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?" amazonka-ml=A description of the most recent details about accessing the MLModel. amazonka-ml+A user-supplied name or description of the MLModel. amazonka-ml$The recipe to use when training the MLModel. The Recipe provides detailed information about the observation data to use during training, and manipulations to perform on the observation data during training.Note:: This parameter is provided as part of the verbose format. amazonka-ml;The schema used by all of the data files referenced by the  DataSource.Note:: This parameter is provided as part of the verbose format. amazonka-ml7The scoring threshold is used in binary classification MLModel models. It marks the boundary between a positive prediction and a negative prediction.Output values greater than or equal to the threshold receive a positive result from the MLModel, such as true. Output values less than the threshold receive a negative response from the MLModel, such as false. amazonka-ml(The time of the most recent edit to the ScoreThreshold'. The time is expressed in epoch time. amazonka-mlUndocumented member. amazonka-ml7The epoch time when Amazon Machine Learning marked the MLModel as  INPROGRESS.  StartedAt isn't available if the MLModel is in the PENDING state. amazonka-mlThe current status of the MLModel5. This element can have one of the following values:PENDING - Amazon Machine Learning (Amazon ML) submitted a request to describe a MLModel. INPROGRESS - The request is processing.FAILED - The request did not run to completion. The ML model isn't usable. COMPLETED& - The request completed successfully.DELETED - The MLModel' is marked as deleted. It isn't usable. amazonka-mlThe ID of the training  DataSource. amazonka-ml)A list of the training parameters in the MLModel7. The list is implemented as a map of key-value pairs.8The following is the current set of training parameters:sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.)The value is an integer that ranges from 100000 to  2147483648. The default value is 33554432. sgd.maxPasses - The number of times that the training process traverses the observations to build the MLModel0. The value is an integer that ranges from 1 to 10000. The default value is 10.sgd.shuffleType - Whether Amazon ML shuffles the training data. Shuffling data improves a model's ability to find the optimal solution for a variety of data types. The valid values are auto and none. The default value is none8. We strongly recommend that you shuffle your data.sgd.l1RegularizationAmount - The coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, start by specifying a small value, such as 1.0E-08.'The value is a double that ranges from 0 to  MAX_DOUBLE. The default is to not use L1 normalization. This parameter can't be used when L2, is specified. Use this parameter sparingly.sgd.l2RegularizationAmount - The coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as 1.0E-08.'The value is a double that ranges from 0 to  MAX_DOUBLE. The default is to not use L2 normalization. This parameter can't be used when L1, is specified. Use this parameter sparingly. amazonka-ml The response's http status code. amazonka-ml amazonka-ml66 (c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred "%&';p& amazonka-mlRepresents the output of a  GetEvaluation operation and describes an  Evaluation.See:  smart constructor. amazonka-mlThe approximate CPU time in milliseconds that Amazon Machine Learning spent processing the  Evaluation3, normalized and scaled on computation resources.  ComputeTime is only available if the  Evaluation is in the  COMPLETED state. amazonka-mlThe time that the  Evaluation3 was created. The time is expressed in epoch time. amazonka-mlThe AWS user account that invoked the evaluation. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account. amazonka-mlThe  DataSource used for this evaluation. amazonka-ml'The evaluation ID which is same as the  EvaluationId in the request. amazonka-ml7The epoch time when Amazon Machine Learning marked the  Evaluation as  COMPLETED or FAILED.  FinishedAt is only available when the  Evaluation is in the  COMPLETED or FAILED state. amazonka-mlThe location of the data file or directory in Amazon Simple Storage Service (Amazon S3). amazonka-ml(The time of the most recent edit to the  Evaluation'. The time is expressed in epoch time. amazonka-ml-A link to the file that contains logs of the CreateEvaluation operation. amazonka-mlThe ID of the MLModel& that was the focus of the evaluation. amazonka-ml>A description of the most recent details about evaluating the MLModel. amazonka-ml+A user-supplied name or description of the  Evaluation. amazonka-mlMeasurements of how well the MLModel1 performed using observations referenced by the  DataSource. One of the following metric is returned based on the type of the MLModel:BinaryAUC: A binary MLModel uses the Area Under the Curve (AUC) technique to measure performance.RegressionRMSE: A regression MLModel uses the Root Mean Square Error (RMSE) technique to measure performance. RMSE measures the difference between predicted and actual values for a single variable."MulticlassAvgFScore: A multiclass MLModel9 uses the F1 score technique to measure performance.For more information about performance metrics, please see the  6https://docs.aws.amazon.com/machine-learning/latest/dg'Amazon Machine Learning Developer Guide. amazonka-ml7The epoch time when Amazon Machine Learning marked the  Evaluation as  INPROGRESS.  StartedAt isn't available if the  Evaluation is in the PENDING state. amazonka-mlThe status of the evaluation. This element can have one of the following values:PENDING - Amazon Machine Language (Amazon ML) submitted a request to evaluate an MLModel. INPROGRESS - The evaluation is underway.FAILED - The request to evaluate an MLModel2 did not run to completion. It is not usable. COMPLETED1 - The evaluation process completed successfully.DELETED - The  Evaluation( is marked as deleted. It is not usable. amazonka-ml The response's http status code. amazonka-mlSee:  smart constructor. amazonka-mlThe ID of the  Evaluation% to retrieve. The evaluation of each MLModel is recorded and cataloged. The ID provides the means to access the information. amazonka-mlCreate a value of " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility:,  - The ID of the  Evaluation% to retrieve. The evaluation of each MLModel is recorded and cataloged. The ID provides the means to access the information. amazonka-mlThe ID of the  Evaluation% to retrieve. The evaluation of each MLModel is recorded and cataloged. The ID provides the means to access the information. amazonka-mlCreate a value of " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility:,  - The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the  Evaluation3, normalized and scaled on computation resources.  ComputeTime is only available if the  Evaluation is in the  COMPLETED state.,  - The time that the  Evaluation3 was created. The time is expressed in epoch time.,  - The AWS user account that invoked the evaluation. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.,  - The  DataSource used for this evaluation., * - The evaluation ID which is same as the  EvaluationId in the request., : - The epoch time when Amazon Machine Learning marked the  Evaluation as  COMPLETED or FAILED.  FinishedAt is only available when the  Evaluation is in the  COMPLETED or FAILED state.,  - The location of the data file or directory in Amazon Simple Storage Service (Amazon S3)., + - The time of the most recent edit to the  Evaluation'. The time is expressed in epoch time., 0 - A link to the file that contains logs of the CreateEvaluation operation.,  - The ID of the MLModel& that was the focus of the evaluation.,  - A description of the most recent details about evaluating the MLModel., . - A user-supplied name or description of the  Evaluation.,  - Measurements of how well the MLModel1 performed using observations referenced by the  DataSource. One of the following metric is returned based on the type of the MLModel:BinaryAUC: A binary MLModel uses the Area Under the Curve (AUC) technique to measure performance.RegressionRMSE: A regression MLModel uses the Root Mean Square Error (RMSE) technique to measure performance. RMSE measures the difference between predicted and actual values for a single variable."MulticlassAvgFScore: A multiclass MLModel9 uses the F1 score technique to measure performance.For more information about performance metrics, please see the  6https://docs.aws.amazon.com/machine-learning/latest/dg'Amazon Machine Learning Developer Guide., : - The epoch time when Amazon Machine Learning marked the  Evaluation as  INPROGRESS.  StartedAt isn't available if the  Evaluation is in the PENDING state.,  - The status of the evaluation. This element can have one of the following values:PENDING - Amazon Machine Language (Amazon ML) submitted a request to evaluate an MLModel. INPROGRESS - The evaluation is underway.FAILED - The request to evaluate an MLModel2 did not run to completion. It is not usable. COMPLETED1 - The evaluation process completed successfully.DELETED - The  Evaluation( is marked as deleted. It is not usable., # - The response's http status code. amazonka-mlThe approximate CPU time in milliseconds that Amazon Machine Learning spent processing the  Evaluation3, normalized and scaled on computation resources.  ComputeTime is only available if the  Evaluation is in the  COMPLETED state. amazonka-mlThe time that the  Evaluation3 was created. The time is expressed in epoch time. amazonka-mlThe AWS user account that invoked the evaluation. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account. amazonka-mlThe  DataSource used for this evaluation. amazonka-ml'The evaluation ID which is same as the  EvaluationId in the request. amazonka-ml7The epoch time when Amazon Machine Learning marked the  Evaluation as  COMPLETED or FAILED.  FinishedAt is only available when the  Evaluation is in the  COMPLETED or FAILED state. amazonka-mlThe location of the data file or directory in Amazon Simple Storage Service (Amazon S3). amazonka-ml(The time of the most recent edit to the  Evaluation'. The time is expressed in epoch time. amazonka-ml-A link to the file that contains logs of the CreateEvaluation operation. amazonka-mlThe ID of the MLModel& that was the focus of the evaluation. amazonka-ml>A description of the most recent details about evaluating the MLModel. amazonka-ml+A user-supplied name or description of the  Evaluation. amazonka-mlMeasurements of how well the MLModel1 performed using observations referenced by the  DataSource. One of the following metric is returned based on the type of the MLModel:BinaryAUC: A binary MLModel uses the Area Under the Curve (AUC) technique to measure performance.RegressionRMSE: A regression MLModel uses the Root Mean Square Error (RMSE) technique to measure performance. RMSE measures the difference between predicted and actual values for a single variable."MulticlassAvgFScore: A multiclass MLModel9 uses the F1 score technique to measure performance.For more information about performance metrics, please see the  6https://docs.aws.amazon.com/machine-learning/latest/dg'Amazon Machine Learning Developer Guide. amazonka-ml7The epoch time when Amazon Machine Learning marked the  Evaluation as  INPROGRESS.  StartedAt isn't available if the  Evaluation is in the PENDING state. amazonka-mlThe status of the evaluation. This element can have one of the following values:PENDING - Amazon Machine Language (Amazon ML) submitted a request to evaluate an MLModel. INPROGRESS - The evaluation is underway.FAILED - The request to evaluate an MLModel2 did not run to completion. It is not usable. COMPLETED1 - The evaluation process completed successfully.DELETED - The  Evaluation( is marked as deleted. It is not usable. amazonka-ml The response's http status code. amazonka-ml amazonka-ml((!(c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred "%&';/ amazonka-mlRepresents the output of a  GetDataSource operation and describes a  DataSource.See:  smart constructor. amazonka-mlThe parameter is true? if statistics need to be generated from the observation data. amazonka-mlThe approximate CPU time in milliseconds that Amazon Machine Learning spent processing the  DataSource3, normalized and scaled on computation resources.  ComputeTime is only available if the  DataSource is in the  COMPLETED state and the ComputeStatistics is set to true. amazonka-mlThe time that the  DataSource3 was created. The time is expressed in epoch time. amazonka-ml$The AWS user account from which the  DataSource was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account. amazonka-mlThe location of the data file or directory in Amazon Simple Storage Service (Amazon S3). amazonka-mlA JSON string that represents the splitting and rearrangement requirement used when this  DataSource was created. amazonka-ml1The total size of observations in the data files. amazonka-mlThe ID assigned to the  DataSource at creation. This value should be identical to the value of the  DataSourceId in the request. amazonka-ml1The schema used by all of the data files of this  DataSource.Note:: This parameter is provided as part of the verbose format. amazonka-ml7The epoch time when Amazon Machine Learning marked the  DataSource as  COMPLETED or FAILED.  FinishedAt is only available when the  DataSource is in the  COMPLETED or FAILED state. amazonka-ml(The time of the most recent edit to the  DataSource'. The time is expressed in epoch time. amazonka-ml&A link to the file containing logs of CreateDataSourceFrom* operations. amazonka-mlThe user-supplied description of the most recent details about creating the  DataSource. amazonka-ml+A user-supplied name or description of the  DataSource. amazonka-ml+The number of data files referenced by the  DataSource. amazonka-ml7The epoch time when Amazon Machine Learning marked the  DataSource as  INPROGRESS.  StartedAt isn't available if the  DataSource is in the PENDING state. amazonka-mlThe current status of the  DataSource5. This element can have one of the following values:PENDING- - Amazon ML submitted a request to create a  DataSource. INPROGRESS$ - The creation process is underway.FAILED - The request to create a  DataSource2 did not run to completion. It is not usable. COMPLETED/ - The creation process completed successfully.DELETED - The  DataSource( is marked as deleted. It is not usable. amazonka-ml The response's http status code. amazonka-mlSee:  smart constructor. amazonka-mlSpecifies whether the  GetDataSource operation should return DataSourceSchema. If true, DataSourceSchema is returned. If false, DataSourceSchema is not returned. amazonka-mlThe ID assigned to the  DataSource at creation. amazonka-mlCreate a value of " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility:,  - Specifies whether the  GetDataSource operation should return DataSourceSchema. If true, DataSourceSchema is returned. If false, DataSourceSchema is not returned.,  - The ID assigned to the  DataSource at creation. amazonka-mlSpecifies whether the  GetDataSource operation should return DataSourceSchema. If true, DataSourceSchema is returned. If false, DataSourceSchema is not returned. amazonka-mlThe ID assigned to the  DataSource at creation. amazonka-mlCreate a value of " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility:,  - The parameter is true? if statistics need to be generated from the observation data.,  - The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the  DataSource3, normalized and scaled on computation resources.  ComputeTime is only available if the  DataSource is in the  COMPLETED state and the ComputeStatistics is set to true.,  - The time that the  DataSource3 was created. The time is expressed in epoch time., ' - The AWS user account from which the  DataSource was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.,  - The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).,  - A JSON string that represents the splitting and rearrangement requirement used when this  DataSource was created., 4 - The total size of observations in the data files.,  - The ID assigned to the  DataSource at creation. This value should be identical to the value of the  DataSourceId in the request., 4 - The schema used by all of the data files of this  DataSource.Note:: This parameter is provided as part of the verbose format., : - The epoch time when Amazon Machine Learning marked the  DataSource as  COMPLETED or FAILED.  FinishedAt is only available when the  DataSource is in the  COMPLETED or FAILED state., + - The time of the most recent edit to the  DataSource'. The time is expressed in epoch time., ) - A link to the file containing logs of CreateDataSourceFrom* operations.,  - The user-supplied description of the most recent details about creating the  DataSource., . - A user-supplied name or description of the  DataSource., . - The number of data files referenced by the  DataSource.,  - Undocumented member.,  - Undocumented member.,  - Undocumented member., : - The epoch time when Amazon Machine Learning marked the  DataSource as  INPROGRESS.  StartedAt isn't available if the  DataSource is in the PENDING state.,  - The current status of the  DataSource5. This element can have one of the following values:PENDING- - Amazon ML submitted a request to create a  DataSource. INPROGRESS$ - The creation process is underway.FAILED - The request to create a  DataSource2 did not run to completion. It is not usable. COMPLETED/ - The creation process completed successfully.DELETED - The  DataSource( is marked as deleted. It is not usable., # - The response's http status code. amazonka-mlThe parameter is true? if statistics need to be generated from the observation data. amazonka-mlThe approximate CPU time in milliseconds that Amazon Machine Learning spent processing the  DataSource3, normalized and scaled on computation resources.  ComputeTime is only available if the  DataSource is in the  COMPLETED state and the ComputeStatistics is set to true. amazonka-mlThe time that the  DataSource3 was created. The time is expressed in epoch time. amazonka-ml$The AWS user account from which the  DataSource was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account. amazonka-mlThe location of the data file or directory in Amazon Simple Storage Service (Amazon S3). amazonka-mlA JSON string that represents the splitting and rearrangement requirement used when this  DataSource was created. amazonka-ml1The total size of observations in the data files. amazonka-mlThe ID assigned to the  DataSource at creation. This value should be identical to the value of the  DataSourceId in the request. amazonka-ml1The schema used by all of the data files of this  DataSource.Note:: This parameter is provided as part of the verbose format. amazonka-ml7The epoch time when Amazon Machine Learning marked the  DataSource as  COMPLETED or FAILED.  FinishedAt is only available when the  DataSource is in the  COMPLETED or FAILED state. amazonka-ml(The time of the most recent edit to the  DataSource'. The time is expressed in epoch time. amazonka-ml&A link to the file containing logs of CreateDataSourceFrom* operations. amazonka-mlThe user-supplied description of the most recent details about creating the  DataSource. amazonka-ml+A user-supplied name or description of the  DataSource. amazonka-ml+The number of data files referenced by the  DataSource. amazonka-mlUndocumented member. amazonka-mlUndocumented member. amazonka-mlUndocumented member. amazonka-ml7The epoch time when Amazon Machine Learning marked the  DataSource as  INPROGRESS.  StartedAt isn't available if the  DataSource is in the PENDING state. amazonka-mlThe current status of the  DataSource5. This element can have one of the following values:PENDING- - Amazon ML submitted a request to create a  DataSource. INPROGRESS$ - The creation process is underway.FAILED - The request to create a  DataSource2 did not run to completion. It is not usable. COMPLETED/ - The creation process completed successfully.DELETED - The  DataSource( is marked as deleted. It is not usable. amazonka-ml The response's http status code. amazonka-ml amazonka-ml44"(c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred "%&';|* amazonka-mlRepresents the output of a GetBatchPrediction operation and describes a BatchPrediction.See:  smart constructor. amazonka-mlThe ID of the  DataSource that was used to create the BatchPrediction. amazonka-mlAn ID assigned to the BatchPrediction at creation. This value should be identical to the value of the BatchPredictionID in the request. amazonka-mlThe approximate CPU time in milliseconds that Amazon Machine Learning spent processing the BatchPrediction3, normalized and scaled on computation resources.  ComputeTime is only available if the BatchPrediction is in the  COMPLETED state. amazonka-mlThe time when the BatchPrediction3 was created. The time is expressed in epoch time. amazonka-ml&The AWS user account that invoked the BatchPrediction. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account. amazonka-ml7The epoch time when Amazon Machine Learning marked the BatchPrediction as  COMPLETED or FAILED.  FinishedAt is only available when the BatchPrediction is in the  COMPLETED or FAILED state. amazonka-mlThe location of the data file or directory in Amazon Simple Storage Service (Amazon S3). amazonka-mlThe number of invalid records that Amazon Machine Learning saw while processing the BatchPrediction. amazonka-ml$The time of the most recent edit to BatchPrediction'. The time is expressed in epoch time. amazonka-ml-A link to the file that contains logs of the CreateBatchPrediction operation. amazonka-mlThe ID of the MLModel% that generated predictions for the BatchPrediction request. amazonka-mlA description of the most recent details about processing the batch prediction request. amazonka-ml+A user-supplied name or description of the BatchPrediction. amazonka-mlThe location of an Amazon S3 bucket or directory to receive the operation results. amazonka-ml7The epoch time when Amazon Machine Learning marked the BatchPrediction as  INPROGRESS.  StartedAt isn't available if the BatchPrediction is in the PENDING state. amazonka-mlThe status of the BatchPrediction,, which can be one of the following values:PENDING - Amazon Machine Learning (Amazon ML) submitted a request to generate batch predictions. INPROGRESS) - The batch predictions are in progress.FAILED - The request to perform a batch prediction did not run to completion. It is not usable. COMPLETED7 - The batch prediction process completed successfully.DELETED - The BatchPrediction- is marked as deleted. It is not usable. amazonka-mlThe number of total records that Amazon Machine Learning saw while processing the BatchPrediction. amazonka-ml The response's http status code. amazonka-mlSee:  smart constructor. amazonka-mlAn ID assigned to the BatchPrediction at creation. amazonka-mlCreate a value of " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility:,  - An ID assigned to the BatchPrediction at creation. amazonka-mlAn ID assigned to the BatchPrediction at creation. amazonka-mlCreate a value of " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility:,  - The ID of the  DataSource that was used to create the BatchPrediction.,  - An ID assigned to the BatchPrediction at creation. This value should be identical to the value of the BatchPredictionID in the request.,  - The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the BatchPrediction3, normalized and scaled on computation resources.  ComputeTime is only available if the BatchPrediction is in the  COMPLETED state.,  - The time when the BatchPrediction3 was created. The time is expressed in epoch time., ) - The AWS user account that invoked the BatchPrediction. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account., : - The epoch time when Amazon Machine Learning marked the BatchPrediction as  COMPLETED or FAILED.  FinishedAt is only available when the BatchPrediction is in the  COMPLETED or FAILED state.,  - The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).,  - The number of invalid records that Amazon Machine Learning saw while processing the BatchPrediction., ' - The time of the most recent edit to BatchPrediction'. The time is expressed in epoch time., 0 - A link to the file that contains logs of the CreateBatchPrediction operation.,  - The ID of the MLModel% that generated predictions for the BatchPrediction request.,  - A description of the most recent details about processing the batch prediction request., . - A user-supplied name or description of the BatchPrediction.,  - The location of an Amazon S3 bucket or directory to receive the operation results., : - The epoch time when Amazon Machine Learning marked the BatchPrediction as  INPROGRESS.  StartedAt isn't available if the BatchPrediction is in the PENDING state.,  - The status of the BatchPrediction,, which can be one of the following values:PENDING - Amazon Machine Learning (Amazon ML) submitted a request to generate batch predictions. INPROGRESS) - The batch predictions are in progress.FAILED - The request to perform a batch prediction did not run to completion. It is not usable. COMPLETED7 - The batch prediction process completed successfully.DELETED - The BatchPrediction- is marked as deleted. It is not usable.,  - The number of total records that Amazon Machine Learning saw while processing the BatchPrediction., # - The response's http status code. amazonka-mlThe ID of the  DataSource that was used to create the BatchPrediction. amazonka-mlAn ID assigned to the BatchPrediction at creation. This value should be identical to the value of the BatchPredictionID in the request. amazonka-mlThe approximate CPU time in milliseconds that Amazon Machine Learning spent processing the BatchPrediction3, normalized and scaled on computation resources.  ComputeTime is only available if the BatchPrediction is in the  COMPLETED state. amazonka-mlThe time when the BatchPrediction3 was created. The time is expressed in epoch time. amazonka-ml&The AWS user account that invoked the BatchPrediction. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account. amazonka-ml7The epoch time when Amazon Machine Learning marked the BatchPrediction as  COMPLETED or FAILED.  FinishedAt is only available when the BatchPrediction is in the  COMPLETED or FAILED state. amazonka-mlThe location of the data file or directory in Amazon Simple Storage Service (Amazon S3). amazonka-mlThe number of invalid records that Amazon Machine Learning saw while processing the BatchPrediction. amazonka-ml$The time of the most recent edit to BatchPrediction'. The time is expressed in epoch time. amazonka-ml-A link to the file that contains logs of the CreateBatchPrediction operation. amazonka-mlThe ID of the MLModel% that generated predictions for the BatchPrediction request. amazonka-mlA description of the most recent details about processing the batch prediction request. amazonka-ml+A user-supplied name or description of the BatchPrediction. amazonka-mlThe location of an Amazon S3 bucket or directory to receive the operation results. amazonka-ml7The epoch time when Amazon Machine Learning marked the BatchPrediction as  INPROGRESS.  StartedAt isn't available if the BatchPrediction is in the PENDING state. amazonka-mlThe status of the BatchPrediction,, which can be one of the following values:PENDING - Amazon Machine Learning (Amazon ML) submitted a request to generate batch predictions. INPROGRESS) - The batch predictions are in progress.FAILED - The request to perform a batch prediction did not run to completion. It is not usable. COMPLETED7 - The batch prediction process completed successfully.DELETED - The BatchPrediction- is marked as deleted. It is not usable. amazonka-mlThe number of total records that Amazon Machine Learning saw while processing the BatchPrediction. amazonka-ml The response's http status code. amazonka-ml amazonka-ml,,#(c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred "%&';͞ amazonka-ml)Amazon ML returns the following elements.See:  smart constructor. amazonka-mlThe ID of the tagged ML object. amazonka-ml!The type of the tagged ML object. amazonka-ml-A list of tags associated with the ML object. amazonka-ml The response's http status code. amazonka-mlSee:  smart constructor. amazonka-ml&The ID of the ML object. For example, exampleModelId. amazonka-mlThe type of the ML object. amazonka-mlCreate a value of " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility:, ) - The ID of the ML object. For example, exampleModelId.,  - The type of the ML object. amazonka-ml&The ID of the ML object. For example, exampleModelId. amazonka-mlThe type of the ML object. amazonka-mlCreate a value of " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility:, " - The ID of the tagged ML object., $ - The type of the tagged ML object., 0 - A list of tags associated with the ML object., # - The response's http status code. amazonka-mlThe ID of the tagged ML object. amazonka-ml!The type of the tagged ML object. amazonka-ml-A list of tags associated with the ML object. amazonka-ml The response's http status code. amazonka-ml amazonka-ml amazonka-ml$(c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred "%&';R  amazonka-mlRepresents the output of a DescribeMLModels2 operation. The content is essentially a list of MLModel.See:  smart constructor. amazonka-mlThe ID of the next page in the paginated results that indicates at least one more page follows. amazonka-ml A list of MLModel that meet the search criteria. amazonka-ml The response's http status code. amazonka-mlSee:  smart constructor. amazonka-mlThe equal to operator. The MLModel results will have FilterVariable5 values that exactly match the value specified with EQ. amazonka-ml7Use one of the following variables to filter a list of MLModel:  CreatedAt - Sets the search criteria to MLModel creation date.Status - Sets the search criteria to MLModel status.Name/ - Sets the search criteria to the contents of MLModel ____ Name.IAMUser - Sets the search criteria to the user account that invoked the MLModel creation.TrainingDataSourceId( - Sets the search criteria to the  DataSource used to train one or more MLModel.RealtimeEndpointStatus# - Sets the search criteria to the MLModel real-time endpoint status. MLModelType - Sets the search criteria to MLModel/ type: binary, regression, or multi-class. Algorithm; - Sets the search criteria to the algorithm that the MLModel uses.TrainingDataURI - Sets the search criteria to the data file(s) used in training a MLModel. The URL can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory. amazonka-ml+The greater than or equal to operator. The MLModel results will have FilterVariable values that are greater than or equal to the value specified with GE. amazonka-mlThe greater than operator. The MLModel results will have FilterVariable8 values that are greater than the value specified with GT. amazonka-ml(The less than or equal to operator. The MLModel results will have FilterVariable values that are less than or equal to the value specified with LE. amazonka-mlThe less than operator. The MLModel results will have FilterVariable5 values that are less than the value specified with LT. amazonka-mlThe number of pages of information to include in the result. The range of acceptable values is 1 through 100. The default value is 100. amazonka-mlThe not equal to operator. The MLModel results will have FilterVariable. values not equal to the value specified with NE. amazonka-ml,The ID of the page in the paginated results. amazonka-ml?A string that is found at the beginning of a variable, such as Name or Id.For example, an MLModel could have the Name 2014-09-09-HolidayGiftMailer. To search for this MLModel , select Name for the FilterVariable+ and any of the following strings for the Prefix:2014-09 2014-09-092014-09-09-Holiday amazonka-mlA two-value parameter that determines the sequence of the resulting list of MLModel.asc3 - Arranges the list in ascending order (A-Z, 0-9).dsc4 - Arranges the list in descending order (Z-A, 9-0).Results are sorted by FilterVariable. amazonka-mlCreate a value of " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility:,  - The equal to operator. The MLModel results will have FilterVariable5 values that exactly match the value specified with EQ., : - Use one of the following variables to filter a list of MLModel:  CreatedAt - Sets the search criteria to MLModel creation date.Status - Sets the search criteria to MLModel status.Name/ - Sets the search criteria to the contents of MLModel ____ Name.IAMUser - Sets the search criteria to the user account that invoked the MLModel creation.TrainingDataSourceId( - Sets the search criteria to the  DataSource used to train one or more MLModel.RealtimeEndpointStatus# - Sets the search criteria to the MLModel real-time endpoint status. MLModelType - Sets the search criteria to MLModel/ type: binary, regression, or multi-class. Algorithm; - Sets the search criteria to the algorithm that the MLModel uses.TrainingDataURI - Sets the search criteria to the data file(s) used in training a MLModel. The URL can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory., . - The greater than or equal to operator. The MLModel results will have FilterVariable values that are greater than or equal to the value specified with GE., " - The greater than operator. The MLModel results will have FilterVariable8 values that are greater than the value specified with GT., + - The less than or equal to operator. The MLModel results will have FilterVariable values that are less than or equal to the value specified with LE.,  - The less than operator. The MLModel results will have FilterVariable5 values that are less than the value specified with LT.,  - The number of pages of information to include in the result. The range of acceptable values is 1 through 100. The default value is 100., " - The not equal to operator. The MLModel results will have FilterVariable. values not equal to the value specified with NE., / - The ID of the page in the paginated results.,  - A string that is found at the beginning of a variable, such as Name or Id.For example, an MLModel could have the Name 2014-09-09-HolidayGiftMailer. To search for this MLModel , select Name for the FilterVariable+ and any of the following strings for the Prefix:2014-09 2014-09-092014-09-09-Holiday,  - A two-value parameter that determines the sequence of the resulting list of MLModel.asc3 - Arranges the list in ascending order (A-Z, 0-9).dsc4 - Arranges the list in descending order (Z-A, 9-0).Results are sorted by FilterVariable. amazonka-mlThe equal to operator. The MLModel results will have FilterVariable5 values that exactly match the value specified with EQ. amazonka-ml7Use one of the following variables to filter a list of MLModel:  CreatedAt - Sets the search criteria to MLModel creation date.Status - Sets the search criteria to MLModel status.Name/ - Sets the search criteria to the contents of MLModel ____ Name.IAMUser - Sets the search criteria to the user account that invoked the MLModel creation.TrainingDataSourceId( - Sets the search criteria to the  DataSource used to train one or more MLModel.RealtimeEndpointStatus# - Sets the search criteria to the MLModel real-time endpoint status. MLModelType - Sets the search criteria to MLModel/ type: binary, regression, or multi-class. Algorithm; - Sets the search criteria to the algorithm that the MLModel uses.TrainingDataURI - Sets the search criteria to the data file(s) used in training a MLModel. The URL can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory. amazonka-ml+The greater than or equal to operator. The MLModel results will have FilterVariable values that are greater than or equal to the value specified with GE. amazonka-mlThe greater than operator. The MLModel results will have FilterVariable8 values that are greater than the value specified with GT. amazonka-ml(The less than or equal to operator. The MLModel results will have FilterVariable values that are less than or equal to the value specified with LE. amazonka-mlThe less than operator. The MLModel results will have FilterVariable5 values that are less than the value specified with LT. amazonka-mlThe number of pages of information to include in the result. The range of acceptable values is 1 through 100. The default value is 100. amazonka-mlThe not equal to operator. The MLModel results will have FilterVariable. values not equal to the value specified with NE. amazonka-ml,The ID of the page in the paginated results. amazonka-ml?A string that is found at the beginning of a variable, such as Name or Id.For example, an MLModel could have the Name 2014-09-09-HolidayGiftMailer. To search for this MLModel , select Name for the FilterVariable+ and any of the following strings for the Prefix:2014-09 2014-09-092014-09-09-Holiday amazonka-mlA two-value parameter that determines the sequence of the resulting list of MLModel.asc3 - Arranges the list in ascending order (A-Z, 0-9).dsc4 - Arranges the list in descending order (Z-A, 9-0).Results are sorted by FilterVariable. amazonka-mlCreate a value of " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility:,  - The ID of the next page in the paginated results that indicates at least one more page follows.,  - A list of MLModel that meet the search criteria., # - The response's http status code. amazonka-mlThe ID of the next page in the paginated results that indicates at least one more page follows. amazonka-ml A list of MLModel that meet the search criteria. amazonka-ml The response's http status code. amazonka-ml""%(c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred "%&';!  amazonka-ml$Represents the query results from a DescribeEvaluations2 operation. The content is essentially a list of  Evaluation.See:  smart constructor. amazonka-mlThe ID of the next page in the paginated results that indicates at least one more page follows. amazonka-ml A list of  Evaluation that meet the search criteria. amazonka-ml The response's http status code. amazonka-mlSee:  smart constructor. amazonka-mlThe equal to operator. The  Evaluation results will have FilterVariable5 values that exactly match the value specified with EQ. amazonka-ml6Use one of the following variable to filter a list of  Evaluation objects: CreatedAt# - Sets the search criteria to the  Evaluation creation date.Status# - Sets the search criteria to the  Evaluation status.Name/ - Sets the search criteria to the contents of  Evaluation ____ Name.IAMUser - Sets the search criteria to the user account that invoked an  Evaluation. MLModelId# - Sets the search criteria to the MLModel that was evaluated. DataSourceId# - Sets the search criteria to the  DataSource used in  Evaluation.DataUri= - Sets the search criteria to the data file(s) used in  Evaluation. The URL can identify either a file or an Amazon Simple Storage Solution (Amazon S3) bucket or directory. amazonka-ml+The greater than or equal to operator. The  Evaluation results will have FilterVariable values that are greater than or equal to the value specified with GE. amazonka-mlThe greater than operator. The  Evaluation results will have FilterVariable8 values that are greater than the value specified with GT. amazonka-ml(The less than or equal to operator. The  Evaluation results will have FilterVariable values that are less than or equal to the value specified with LE. amazonka-mlThe less than operator. The  Evaluation results will have FilterVariable5 values that are less than the value specified with LT. amazonka-mlThe maximum number of  Evaluation to include in the result. amazonka-mlThe not equal to operator. The  Evaluation results will have FilterVariable. values not equal to the value specified with NE. amazonka-ml,The ID of the page in the paginated results. amazonka-ml?A string that is found at the beginning of a variable, such as Name or Id.For example, an  Evaluation could have the Name 2014-09-09-HolidayGiftMailer. To search for this  Evaluation , select Name for the FilterVariable+ and any of the following strings for the Prefix:2014-09 2014-09-092014-09-09-Holiday amazonka-mlA two-value parameter that determines the sequence of the resulting list of  Evaluation.asc3 - Arranges the list in ascending order (A-Z, 0-9).dsc4 - Arranges the list in descending order (Z-A, 9-0).Results are sorted by FilterVariable. amazonka-mlCreate a value of " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility:,  - The equal to operator. The  Evaluation results will have FilterVariable5 values that exactly match the value specified with EQ., 9 - Use one of the following variable to filter a list of  Evaluation objects: CreatedAt# - Sets the search criteria to the  Evaluation creation date.Status# - Sets the search criteria to the  Evaluation status.Name/ - Sets the search criteria to the contents of  Evaluation ____ Name.IAMUser - Sets the search criteria to the user account that invoked an  Evaluation. MLModelId# - Sets the search criteria to the MLModel that was evaluated. DataSourceId# - Sets the search criteria to the  DataSource used in  Evaluation.DataUri= - Sets the search criteria to the data file(s) used in  Evaluation. The URL can identify either a file or an Amazon Simple Storage Solution (Amazon S3) bucket or directory., . - The greater than or equal to operator. The  Evaluation results will have FilterVariable values that are greater than or equal to the value specified with GE., " - The greater than operator. The  Evaluation results will have FilterVariable8 values that are greater than the value specified with GT., + - The less than or equal to operator. The  Evaluation results will have FilterVariable values that are less than or equal to the value specified with LE.,  - The less than operator. The  Evaluation results will have FilterVariable5 values that are less than the value specified with LT.,  - The maximum number of  Evaluation to include in the result., " - The not equal to operator. The  Evaluation results will have FilterVariable. values not equal to the value specified with NE., / - The ID of the page in the paginated results.,  - A string that is found at the beginning of a variable, such as Name or Id.For example, an  Evaluation could have the Name 2014-09-09-HolidayGiftMailer. To search for this  Evaluation , select Name for the FilterVariable+ and any of the following strings for the Prefix:2014-09 2014-09-092014-09-09-Holiday,  - A two-value parameter that determines the sequence of the resulting list of  Evaluation.asc3 - Arranges the list in ascending order (A-Z, 0-9).dsc4 - Arranges the list in descending order (Z-A, 9-0).Results are sorted by FilterVariable. amazonka-mlThe equal to operator. The  Evaluation results will have FilterVariable5 values that exactly match the value specified with EQ. amazonka-ml6Use one of the following variable to filter a list of  Evaluation objects: CreatedAt# - Sets the search criteria to the  Evaluation creation date.Status# - Sets the search criteria to the  Evaluation status.Name/ - Sets the search criteria to the contents of  Evaluation ____ Name.IAMUser - Sets the search criteria to the user account that invoked an  Evaluation. MLModelId# - Sets the search criteria to the MLModel that was evaluated. DataSourceId# - Sets the search criteria to the  DataSource used in  Evaluation.DataUri= - Sets the search criteria to the data file(s) used in  Evaluation. The URL can identify either a file or an Amazon Simple Storage Solution (Amazon S3) bucket or directory. amazonka-ml+The greater than or equal to operator. The  Evaluation results will have FilterVariable values that are greater than or equal to the value specified with GE. amazonka-mlThe greater than operator. The  Evaluation results will have FilterVariable8 values that are greater than the value specified with GT. amazonka-ml(The less than or equal to operator. The  Evaluation results will have FilterVariable values that are less than or equal to the value specified with LE. amazonka-mlThe less than operator. The  Evaluation results will have FilterVariable5 values that are less than the value specified with LT. amazonka-mlThe maximum number of  Evaluation to include in the result. amazonka-mlThe not equal to operator. The  Evaluation results will have FilterVariable. values not equal to the value specified with NE. amazonka-ml,The ID of the page in the paginated results. amazonka-ml?A string that is found at the beginning of a variable, such as Name or Id.For example, an  Evaluation could have the Name 2014-09-09-HolidayGiftMailer. To search for this  Evaluation , select Name for the FilterVariable+ and any of the following strings for the Prefix:2014-09 2014-09-092014-09-09-Holiday amazonka-mlA two-value parameter that determines the sequence of the resulting list of  Evaluation.asc3 - Arranges the list in ascending order (A-Z, 0-9).dsc4 - Arranges the list in descending order (Z-A, 9-0).Results are sorted by FilterVariable. amazonka-mlCreate a value of " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility:,  - The ID of the next page in the paginated results that indicates at least one more page follows.,  - A list of  Evaluation that meet the search criteria., # - The response's http status code. amazonka-mlThe ID of the next page in the paginated results that indicates at least one more page follows. amazonka-ml A list of  Evaluation that meet the search criteria. amazonka-ml The response's http status code. amazonka-ml""&(c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred "%&';H  amazonka-mlRepresents the query results from a DescribeDataSources operation. The content is essentially a list of  DataSource.See:   smart constructor. amazonka-mlAn ID of the next page in the paginated results that indicates at least one more page follows. amazonka-ml A list of  DataSource that meet the search criteria. amazonka-ml The response's http status code. amazonka-mlSee:  smart constructor. amazonka-mlThe equal to operator. The  DataSource results will have FilterVariable5 values that exactly match the value specified with EQ. amazonka-ml7Use one of the following variables to filter a list of  DataSource: CreatedAt - Sets the search criteria to  DataSource creation dates.Status - Sets the search criteria to  DataSource statuses.Name/ - Sets the search criteria to the contents of  DataSource Name.DataUri - Sets the search criteria to the URI of data files used to create the  DataSource. The URI can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory.IAMUser - Sets the search criteria to the user account that invoked the  DataSource creation. amazonka-ml+The greater than or equal to operator. The  DataSource results will have FilterVariable values that are greater than or equal to the value specified with GE. amazonka-mlThe greater than operator. The  DataSource results will have FilterVariable8 values that are greater than the value specified with GT. amazonka-ml(The less than or equal to operator. The  DataSource results will have FilterVariable values that are less than or equal to the value specified with LE. amazonka-mlThe less than operator. The  DataSource results will have FilterVariable5 values that are less than the value specified with LT. amazonka-mlThe maximum number of  DataSource to include in the result. amazonka-mlThe not equal to operator. The  DataSource results will have FilterVariable. values not equal to the value specified with NE. amazonka-ml,The ID of the page in the paginated results. amazonka-ml?A string that is found at the beginning of a variable, such as Name or Id.For example, a  DataSource could have the Name 2014-09-09-HolidayGiftMailer. To search for this  DataSource , select Name for the FilterVariable+ and any of the following strings for the Prefix:2014-09 2014-09-092014-09-09-Holiday amazonka-mlA two-value parameter that determines the sequence of the resulting list of  DataSource.asc3 - Arranges the list in ascending order (A-Z, 0-9).dsc4 - Arranges the list in descending order (Z-A, 9-0).Results are sorted by FilterVariable. amazonka-mlCreate a value of " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility:,   - The equal to operator. The  DataSource results will have FilterVariable5 values that exactly match the value specified with EQ.,  : - Use one of the following variables to filter a list of  DataSource: CreatedAt - Sets the search criteria to  DataSource creation dates.Status - Sets the search criteria to  DataSource statuses.Name/ - Sets the search criteria to the contents of  DataSource Name.DataUri - Sets the search criteria to the URI of data files used to create the  DataSource. The URI can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory.IAMUser - Sets the search criteria to the user account that invoked the  DataSource creation.,  . - The greater than or equal to operator. The  DataSource results will have FilterVariable values that are greater than or equal to the value specified with GE.,  " - The greater than operator. The  DataSource results will have FilterVariable8 values that are greater than the value specified with GT.,  + - The less than or equal to operator. The  DataSource results will have FilterVariable values that are less than or equal to the value specified with LE.,   - The less than operator. The  DataSource results will have FilterVariable5 values that are less than the value specified with LT.,   - The maximum number of  DataSource to include in the result.,  " - The not equal to operator. The  DataSource results will have FilterVariable. values not equal to the value specified with NE.,  / - The ID of the page in the paginated results.,   - A string that is found at the beginning of a variable, such as Name or Id.For example, a  DataSource could have the Name 2014-09-09-HolidayGiftMailer. To search for this  DataSource , select Name for the FilterVariable+ and any of the following strings for the Prefix:2014-09 2014-09-092014-09-09-Holiday,   - A two-value parameter that determines the sequence of the resulting list of  DataSource.asc3 - Arranges the list in ascending order (A-Z, 0-9).dsc4 - Arranges the list in descending order (Z-A, 9-0).Results are sorted by FilterVariable.  amazonka-mlThe equal to operator. The  DataSource results will have FilterVariable5 values that exactly match the value specified with EQ.  amazonka-ml7Use one of the following variables to filter a list of  DataSource: CreatedAt - Sets the search criteria to  DataSource creation dates.Status - Sets the search criteria to  DataSource statuses.Name/ - Sets the search criteria to the contents of  DataSource Name.DataUri - Sets the search criteria to the URI of data files used to create the  DataSource. The URI can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory.IAMUser - Sets the search criteria to the user account that invoked the  DataSource creation.  amazonka-ml+The greater than or equal to operator. The  DataSource results will have FilterVariable values that are greater than or equal to the value specified with GE.  amazonka-mlThe greater than operator. The  DataSource results will have FilterVariable8 values that are greater than the value specified with GT.  amazonka-ml(The less than or equal to operator. The  DataSource results will have FilterVariable values that are less than or equal to the value specified with LE.  amazonka-mlThe less than operator. The  DataSource results will have FilterVariable5 values that are less than the value specified with LT.  amazonka-mlThe maximum number of  DataSource to include in the result.  amazonka-mlThe not equal to operator. The  DataSource results will have FilterVariable. values not equal to the value specified with NE.  amazonka-ml,The ID of the page in the paginated results.  amazonka-ml?A string that is found at the beginning of a variable, such as Name or Id.For example, a  DataSource could have the Name 2014-09-09-HolidayGiftMailer. To search for this  DataSource , select Name for the FilterVariable+ and any of the following strings for the Prefix:2014-09 2014-09-092014-09-09-Holiday  amazonka-mlA two-value parameter that determines the sequence of the resulting list of  DataSource.asc3 - Arranges the list in ascending order (A-Z, 0-9).dsc4 - Arranges the list in descending order (Z-A, 9-0).Results are sorted by FilterVariable.  amazonka-mlCreate a value of " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility:,   - An ID of the next page in the paginated results that indicates at least one more page follows.,   - A list of  DataSource that meet the search criteria.,  # - The response's http status code.  amazonka-mlAn ID of the next page in the paginated results that indicates at least one more page follows.  amazonka-ml A list of  DataSource that meet the search criteria.  amazonka-ml The response's http status code.  amazonka-ml" "  '(c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred "%&';s7  amazonka-mlRepresents the output of a DescribeBatchPredictions2 operation. The content is essentially a list of BatchPredictions.See:   smart constructor.  amazonka-mlThe ID of the next page in the paginated results that indicates at least one more page follows.  amazonka-ml A list of BatchPrediction' objects that meet the search criteria.  amazonka-ml The response's http status code.  amazonka-mlSee:   smart constructor.  amazonka-mlThe equal to operator. The BatchPrediction results will have FilterVariable5 values that exactly match the value specified with EQ.  amazonka-ml8Use one of the following variables to filter a list of BatchPrediction: CreatedAt# - Sets the search criteria to the BatchPrediction creation date.Status# - Sets the search criteria to the BatchPrediction status.Name8 - Sets the search criteria to the contents of the BatchPrediction ____ Name.IAMUser - Sets the search criteria to the user account that invoked the BatchPrediction creation. MLModelId# - Sets the search criteria to the MLModel used in the BatchPrediction. DataSourceId# - Sets the search criteria to the  DataSource used in the BatchPrediction.DataURI - Sets the search criteria to the data file(s) used in the BatchPrediction. The URL can identify either a file or an Amazon Simple Storage Solution (Amazon S3) bucket or directory.  amazonka-ml+The greater than or equal to operator. The BatchPrediction results will have FilterVariable values that are greater than or equal to the value specified with GE.  amazonka-mlThe greater than operator. The BatchPrediction results will have FilterVariable8 values that are greater than the value specified with GT.  amazonka-ml(The less than or equal to operator. The BatchPrediction results will have FilterVariable values that are less than or equal to the value specified with LE.  amazonka-mlThe less than operator. The BatchPrediction results will have FilterVariable5 values that are less than the value specified with LT.  amazonka-mlThe number of pages of information to include in the result. The range of acceptable values is 1 through 100. The default value is 100.  amazonka-mlThe not equal to operator. The BatchPrediction results will have FilterVariable. values not equal to the value specified with NE.  amazonka-ml+An ID of the page in the paginated results.  amazonka-ml?A string that is found at the beginning of a variable, such as Name or Id.For example, a Batch Prediction operation could have the Name 2014-09-09-HolidayGiftMailer. To search for this BatchPrediction , select Name for the FilterVariable+ and any of the following strings for the Prefix:2014-09 2014-09-092014-09-09-Holiday  amazonka-mlA two-value parameter that determines the sequence of the resulting list of MLModels.asc3 - Arranges the list in ascending order (A-Z, 0-9).dsc4 - Arranges the list in descending order (Z-A, 9-0).Results are sorted by FilterVariable.  amazonka-mlCreate a value of  " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility: ,   - The equal to operator. The BatchPrediction results will have FilterVariable5 values that exactly match the value specified with EQ. ,  ; - Use one of the following variables to filter a list of BatchPrediction: CreatedAt# - Sets the search criteria to the BatchPrediction creation date.Status# - Sets the search criteria to the BatchPrediction status.Name8 - Sets the search criteria to the contents of the BatchPrediction ____ Name.IAMUser - Sets the search criteria to the user account that invoked the BatchPrediction creation. MLModelId# - Sets the search criteria to the MLModel used in the BatchPrediction. DataSourceId# - Sets the search criteria to the  DataSource used in the BatchPrediction.DataURI - Sets the search criteria to the data file(s) used in the BatchPrediction. The URL can identify either a file or an Amazon Simple Storage Solution (Amazon S3) bucket or directory. ,  . - The greater than or equal to operator. The BatchPrediction results will have FilterVariable values that are greater than or equal to the value specified with GE. ,  " - The greater than operator. The BatchPrediction results will have FilterVariable8 values that are greater than the value specified with GT. ,  + - The less than or equal to operator. The BatchPrediction results will have FilterVariable values that are less than or equal to the value specified with LE. ,   - The less than operator. The BatchPrediction results will have FilterVariable5 values that are less than the value specified with LT. ,   - The number of pages of information to include in the result. The range of acceptable values is 1 through 100. The default value is 100. ,  " - The not equal to operator. The BatchPrediction results will have FilterVariable. values not equal to the value specified with NE. ,  . - An ID of the page in the paginated results. ,   - A string that is found at the beginning of a variable, such as Name or Id.For example, a Batch Prediction operation could have the Name 2014-09-09-HolidayGiftMailer. To search for this BatchPrediction , select Name for the FilterVariable+ and any of the following strings for the Prefix:2014-09 2014-09-092014-09-09-Holiday ,   - A two-value parameter that determines the sequence of the resulting list of MLModels.asc3 - Arranges the list in ascending order (A-Z, 0-9).dsc4 - Arranges the list in descending order (Z-A, 9-0).Results are sorted by FilterVariable.  amazonka-mlThe equal to operator. The BatchPrediction results will have FilterVariable5 values that exactly match the value specified with EQ.  amazonka-ml8Use one of the following variables to filter a list of BatchPrediction: CreatedAt# - Sets the search criteria to the BatchPrediction creation date.Status# - Sets the search criteria to the BatchPrediction status.Name8 - Sets the search criteria to the contents of the BatchPrediction ____ Name.IAMUser - Sets the search criteria to the user account that invoked the BatchPrediction creation. MLModelId# - Sets the search criteria to the MLModel used in the BatchPrediction. DataSourceId# - Sets the search criteria to the  DataSource used in the BatchPrediction.DataURI - Sets the search criteria to the data file(s) used in the BatchPrediction. The URL can identify either a file or an Amazon Simple Storage Solution (Amazon S3) bucket or directory.  amazonka-ml+The greater than or equal to operator. The BatchPrediction results will have FilterVariable values that are greater than or equal to the value specified with GE.  amazonka-mlThe greater than operator. The BatchPrediction results will have FilterVariable8 values that are greater than the value specified with GT.  amazonka-ml(The less than or equal to operator. The BatchPrediction results will have FilterVariable values that are less than or equal to the value specified with LE.  amazonka-mlThe less than operator. The BatchPrediction results will have FilterVariable5 values that are less than the value specified with LT.  amazonka-mlThe number of pages of information to include in the result. The range of acceptable values is 1 through 100. The default value is 100.  amazonka-mlThe not equal to operator. The BatchPrediction results will have FilterVariable. values not equal to the value specified with NE.  amazonka-ml+An ID of the page in the paginated results.  amazonka-ml?A string that is found at the beginning of a variable, such as Name or Id.For example, a Batch Prediction operation could have the Name 2014-09-09-HolidayGiftMailer. To search for this BatchPrediction , select Name for the FilterVariable+ and any of the following strings for the Prefix:2014-09 2014-09-092014-09-09-Holiday  amazonka-mlA two-value parameter that determines the sequence of the resulting list of MLModels.asc3 - Arranges the list in ascending order (A-Z, 0-9).dsc4 - Arranges the list in descending order (Z-A, 9-0).Results are sorted by FilterVariable.  amazonka-mlCreate a value of  " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility: ,   - The ID of the next page in the paginated results that indicates at least one more page follows. ,   - A list of BatchPrediction' objects that meet the search criteria. ,  # - The response's http status code.  amazonka-mlThe ID of the next page in the paginated results that indicates at least one more page follows.  amazonka-ml A list of BatchPrediction' objects that meet the search criteria.  amazonka-ml The response's http status code.  amazonka-ml " " ((c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred "%&';|  amazonka-ml)Amazon ML returns the following elements.See:   smart constructor.  amazonka-ml5The ID of the ML object from which tags were deleted.  amazonka-ml7The type of the ML object from which tags were deleted.  amazonka-ml The response's http status code.  amazonka-mlSee:   smart constructor.  amazonka-mlOne or more tags to delete.  amazonka-ml-The ID of the tagged ML object. For example, exampleModelId.  amazonka-ml!The type of the tagged ML object.  amazonka-mlCreate a value of  " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility: ,   - One or more tags to delete. ,  0 - The ID of the tagged ML object. For example, exampleModelId. ,  $ - The type of the tagged ML object.  amazonka-mlOne or more tags to delete.  amazonka-ml-The ID of the tagged ML object. For example, exampleModelId.  amazonka-ml!The type of the tagged ML object.  amazonka-mlCreate a value of  " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility: ,  8 - The ID of the ML object from which tags were deleted. ,  : - The type of the ML object from which tags were deleted. ,  # - The response's http status code.  amazonka-ml5The ID of the ML object from which tags were deleted.  amazonka-ml7The type of the ML object from which tags were deleted.  amazonka-ml The response's http status code.  amazonka-ml  amazonka-ml  amazonka-ml   )(c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred "%&';  amazonka-mlRepresents the output of an DeleteRealtimeEndpoint operation.The result contains the  MLModelId' and the endpoint information for the MLModel.See:   smart constructor.  amazonka-ml0A user-supplied ID that uniquely identifies the MLModel6. This value should be identical to the value of the  MLModelId in the request.  amazonka-ml The endpoint information of the MLModel  amazonka-ml The response's http status code.  amazonka-mlSee:   smart constructor.  amazonka-mlThe ID assigned to the MLModel during creation.  amazonka-mlCreate a value of  " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility: ,   - The ID assigned to the MLModel during creation.  amazonka-mlThe ID assigned to the MLModel during creation.  amazonka-mlCreate a value of  " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility: ,  3 - A user-supplied ID that uniquely identifies the MLModel6. This value should be identical to the value of the  MLModelId in the request. ,  # - The endpoint information of the MLModel ,  # - The response's http status code.  amazonka-ml0A user-supplied ID that uniquely identifies the MLModel6. This value should be identical to the value of the  MLModelId in the request.  amazonka-ml The endpoint information of the MLModel  amazonka-ml The response's http status code.  amazonka-ml  amazonka-ml   *(c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred "%&';  amazonka-mlRepresents the output of a  DeleteMLModel operation.You can use the  GetMLModel' operation and check the value of the Status parameter to see whether an MLModel is marked as DELETED.See:   smart constructor.  amazonka-ml0A user-supplied ID that uniquely identifies the MLModel6. This value should be identical to the value of the  MLModelID in the request.  amazonka-ml The response's http status code.  amazonka-mlSee:   smart constructor.  amazonka-ml0A user-supplied ID that uniquely identifies the MLModel.  amazonka-mlCreate a value of  " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility: ,  3 - A user-supplied ID that uniquely identifies the MLModel.  amazonka-ml0A user-supplied ID that uniquely identifies the MLModel.  amazonka-mlCreate a value of  " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility: ,  3 - A user-supplied ID that uniquely identifies the MLModel6. This value should be identical to the value of the  MLModelID in the request. ,  # - The response's http status code.  amazonka-ml0A user-supplied ID that uniquely identifies the MLModel6. This value should be identical to the value of the  MLModelID in the request.  amazonka-ml The response's http status code.  amazonka-ml  amazonka-ml +(c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred "%&';  amazonka-mlRepresents the output of a DeleteEvaluation operation. The output indicates that Amazon Machine Learning (Amazon ML) received the request.You can use the  GetEvaluation' operation and check the value of the Status parameter to see whether an  Evaluation is marked as DELETED.See:   smart constructor.  amazonka-ml0A user-supplied ID that uniquely identifies the  Evaluation6. This value should be identical to the value of the  EvaluationId in the request.  amazonka-ml The response's http status code.  amazonka-mlSee:   smart constructor.  amazonka-ml0A user-supplied ID that uniquely identifies the  Evaluation to delete.  amazonka-mlCreate a value of  " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility: ,  3 - A user-supplied ID that uniquely identifies the  Evaluation to delete.  amazonka-ml0A user-supplied ID that uniquely identifies the  Evaluation to delete.  amazonka-mlCreate a value of  " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility: ,  3 - A user-supplied ID that uniquely identifies the  Evaluation6. This value should be identical to the value of the  EvaluationId in the request. ,  # - The response's http status code.  amazonka-ml0A user-supplied ID that uniquely identifies the  Evaluation6. This value should be identical to the value of the  EvaluationId in the request.  amazonka-ml The response's http status code.  amazonka-ml  amazonka-ml ,(c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred "%&';<  amazonka-mlRepresents the output of a DeleteDataSource operation.See:   smart constructor.  amazonka-ml0A user-supplied ID that uniquely identifies the  DataSource6. This value should be identical to the value of the  DataSourceID in the request.  amazonka-ml The response's http status code.  amazonka-mlSee:   smart constructor.  amazonka-ml0A user-supplied ID that uniquely identifies the  DataSource.  amazonka-mlCreate a value of  " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility: ,  3 - A user-supplied ID that uniquely identifies the  DataSource.  amazonka-ml0A user-supplied ID that uniquely identifies the  DataSource.  amazonka-mlCreate a value of  " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility: ,  3 - A user-supplied ID that uniquely identifies the  DataSource6. This value should be identical to the value of the  DataSourceID in the request. ,  # - The response's http status code.  amazonka-ml0A user-supplied ID that uniquely identifies the  DataSource6. This value should be identical to the value of the  DataSourceID in the request.  amazonka-ml The response's http status code.  amazonka-ml  amazonka-ml -(c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred "%&';[  amazonka-mlRepresents the output of a DeleteBatchPrediction operation.You can use the GetBatchPrediction' operation and check the value of the Status parameter to see whether a BatchPrediction is marked as DELETED.See:   smart constructor.  amazonka-ml0A user-supplied ID that uniquely identifies the BatchPrediction6. This value should be identical to the value of the BatchPredictionID in the request.  amazonka-ml The response's http status code.  amazonka-mlSee:   smart constructor.  amazonka-ml0A user-supplied ID that uniquely identifies the BatchPrediction.  amazonka-mlCreate a value of  " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility: ,  3 - A user-supplied ID that uniquely identifies the BatchPrediction.  amazonka-ml0A user-supplied ID that uniquely identifies the BatchPrediction.  amazonka-mlCreate a value of  " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility: ,  3 - A user-supplied ID that uniquely identifies the BatchPrediction6. This value should be identical to the value of the BatchPredictionID in the request. ,  # - The response's http status code.  amazonka-ml0A user-supplied ID that uniquely identifies the BatchPrediction6. This value should be identical to the value of the BatchPredictionID in the request.  amazonka-ml The response's http status code.  amazonka-ml  amazonka-ml .(c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred "%&';0  amazonka-mlRepresents the output of an CreateRealtimeEndpoint operation.The result contains the  MLModelId' and the endpoint information for the MLModel.Note:2 The endpoint information includes the URI of the MLModel; that is, the location to send online prediction requests for the specified MLModel.See:   smart constructor.  amazonka-ml0A user-supplied ID that uniquely identifies the MLModel6. This value should be identical to the value of the  MLModelId in the request.  amazonka-ml The endpoint information of the MLModel  amazonka-ml The response's http status code.  amazonka-mlSee:   smart constructor.  amazonka-mlThe ID assigned to the MLModel during creation.  amazonka-mlCreate a value of  " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility: ,   - The ID assigned to the MLModel during creation.  amazonka-mlThe ID assigned to the MLModel during creation.  amazonka-mlCreate a value of  " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility: ,  3 - A user-supplied ID that uniquely identifies the MLModel6. This value should be identical to the value of the  MLModelId in the request. ,  # - The endpoint information of the MLModel ,  # - The response's http status code.  amazonka-ml0A user-supplied ID that uniquely identifies the MLModel6. This value should be identical to the value of the  MLModelId in the request.  amazonka-ml The endpoint information of the MLModel  amazonka-ml The response's http status code.  amazonka-ml  amazonka-ml   /(c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred "%&';  amazonka-mlRepresents the output of a  CreateMLModel operation, and is an acknowledgement that Amazon ML received the request.The  CreateMLModel operation is asynchronous. You can poll for status updates by using the  GetMLModel operation and checking the Status parameter.See:   smart constructor.  amazonka-ml0A user-supplied ID that uniquely identifies the MLModel6. This value should be identical to the value of the  MLModelId in the request.  amazonka-ml The response's http status code.  amazonka-mlSee:   smart constructor.  amazonka-ml+A user-supplied name or description of the MLModel.  amazonka-ml)A list of the training parameters in the MLModel7. The list is implemented as a map of key-value pairs.8The following is the current set of training parameters:sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.)The value is an integer that ranges from 100000 to  2147483648. The default value is 33554432. sgd.maxPasses - The number of times that the training process traverses the observations to build the MLModel0. The value is an integer that ranges from 1 to 10000. The default value is 10.sgd.shuffleType - Whether Amazon ML shuffles the training data. Shuffling the data improves a model's ability to find the optimal solution for a variety of data types. The valid values are auto and none. The default value is none8. We strongly recommend that you shuffle your data.sgd.l1RegularizationAmount - The coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, start by specifying a small value, such as 1.0E-08.'The value is a double that ranges from 0 to  MAX_DOUBLE. The default is to not use L1 normalization. This parameter can't be used when L2, is specified. Use this parameter sparingly.sgd.l2RegularizationAmount - The coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as 1.0E-08.'The value is a double that ranges from 0 to  MAX_DOUBLE. The default is to not use L2 normalization. This parameter can't be used when L1, is specified. Use this parameter sparingly.  amazonka-ml!The data recipe for creating the MLModel. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default.  amazonka-mlThe Amazon Simple Storage Service (Amazon S3) location and file name that contains the MLModel recipe. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default.  amazonka-ml0A user-supplied ID that uniquely identifies the MLModel.  amazonka-ml.The category of supervised learning that this MLModel0 will address. Choose from the following types:Choose  REGRESSION if the MLModel. will be used to predict a numeric value.Choose BINARY if the MLModel result has two possible values.Choose  MULTICLASS if the MLModel, result has a limited number of values.For more information, see the  6https://docs.aws.amazon.com/machine-learning/latest/dg'Amazon Machine Learning Developer Guide.  amazonka-mlThe  DataSource" that points to the training data.  amazonka-mlCreate a value of  " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility: ,  . - A user-supplied name or description of the MLModel. ,  , - A list of the training parameters in the MLModel7. The list is implemented as a map of key-value pairs.8The following is the current set of training parameters:sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.)The value is an integer that ranges from 100000 to  2147483648. The default value is 33554432. sgd.maxPasses - The number of times that the training process traverses the observations to build the MLModel0. The value is an integer that ranges from 1 to 10000. The default value is 10.sgd.shuffleType - Whether Amazon ML shuffles the training data. Shuffling the data improves a model's ability to find the optimal solution for a variety of data types. The valid values are auto and none. The default value is none8. We strongly recommend that you shuffle your data.sgd.l1RegularizationAmount - The coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, start by specifying a small value, such as 1.0E-08.'The value is a double that ranges from 0 to  MAX_DOUBLE. The default is to not use L1 normalization. This parameter can't be used when L2, is specified. Use this parameter sparingly.sgd.l2RegularizationAmount - The coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as 1.0E-08.'The value is a double that ranges from 0 to  MAX_DOUBLE. The default is to not use L2 normalization. This parameter can't be used when L1, is specified. Use this parameter sparingly. ,  $ - The data recipe for creating the MLModel. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default. ,   - The Amazon Simple Storage Service (Amazon S3) location and file name that contains the MLModel recipe. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default. ,  3 - A user-supplied ID that uniquely identifies the MLModel. ,  1 - The category of supervised learning that this MLModel0 will address. Choose from the following types:Choose  REGRESSION if the MLModel. will be used to predict a numeric value.Choose BINARY if the MLModel result has two possible values.Choose  MULTICLASS if the MLModel, result has a limited number of values.For more information, see the  6https://docs.aws.amazon.com/machine-learning/latest/dg'Amazon Machine Learning Developer Guide. ,   - The  DataSource" that points to the training data.  amazonka-ml+A user-supplied name or description of the MLModel.  amazonka-ml)A list of the training parameters in the MLModel7. The list is implemented as a map of key-value pairs.8The following is the current set of training parameters:sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.)The value is an integer that ranges from 100000 to  2147483648. The default value is 33554432. sgd.maxPasses - The number of times that the training process traverses the observations to build the MLModel0. The value is an integer that ranges from 1 to 10000. The default value is 10.sgd.shuffleType - Whether Amazon ML shuffles the training data. Shuffling the data improves a model's ability to find the optimal solution for a variety of data types. The valid values are auto and none. The default value is none8. We strongly recommend that you shuffle your data.sgd.l1RegularizationAmount - The coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, start by specifying a small value, such as 1.0E-08.'The value is a double that ranges from 0 to  MAX_DOUBLE. The default is to not use L1 normalization. This parameter can't be used when L2, is specified. Use this parameter sparingly.sgd.l2RegularizationAmount - The coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as 1.0E-08.'The value is a double that ranges from 0 to  MAX_DOUBLE. The default is to not use L2 normalization. This parameter can't be used when L1, is specified. Use this parameter sparingly.  amazonka-ml!The data recipe for creating the MLModel. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default.  amazonka-mlThe Amazon Simple Storage Service (Amazon S3) location and file name that contains the MLModel recipe. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default.  amazonka-ml0A user-supplied ID that uniquely identifies the MLModel.  amazonka-ml.The category of supervised learning that this MLModel0 will address. Choose from the following types:Choose  REGRESSION if the MLModel. will be used to predict a numeric value.Choose BINARY if the MLModel result has two possible values.Choose  MULTICLASS if the MLModel, result has a limited number of values.For more information, see the  6https://docs.aws.amazon.com/machine-learning/latest/dg'Amazon Machine Learning Developer Guide.  amazonka-mlThe  DataSource" that points to the training data.  amazonka-mlCreate a value of  " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility: ,  3 - A user-supplied ID that uniquely identifies the MLModel6. This value should be identical to the value of the  MLModelId in the request. ,  # - The response's http status code.  amazonka-ml0A user-supplied ID that uniquely identifies the MLModel6. This value should be identical to the value of the  MLModelId in the request.  amazonka-ml The response's http status code.  amazonka-ml  amazonka-ml  amazonka-ml  amazonka-ml   0(c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred "%&';  amazonka-mlRepresents the output of a CreateEvaluation operation, and is an acknowledgement that Amazon ML received the request.CreateEvaluation operation is asynchronous. You can poll for status updates by using the GetEvcaluation operation and checking the Status parameter.See:   smart constructor.  amazonka-ml2The user-supplied ID that uniquely identifies the  Evaluation6. This value should be identical to the value of the  EvaluationId in the request.  amazonka-ml The response's http status code.  amazonka-mlSee:   smart constructor.  amazonka-ml+A user-supplied name or description of the  Evaluation.  amazonka-ml0A user-supplied ID that uniquely identifies the  Evaluation.  amazonka-mlThe ID of the MLModel to evaluate. The schema used in creating the MLModel must match the schema of the  DataSource used in the  Evaluation.  amazonka-mlThe ID of the  DataSource( for the evaluation. The schema of the  DataSource* must match the schema used to create the MLModel.  amazonka-mlCreate a value of  " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility: ,  . - A user-supplied name or description of the  Evaluation. ,  3 - A user-supplied ID that uniquely identifies the  Evaluation. ,   - The ID of the MLModel to evaluate. The schema used in creating the MLModel must match the schema of the  DataSource used in the  Evaluation. ,   - The ID of the  DataSource( for the evaluation. The schema of the  DataSource* must match the schema used to create the MLModel.  amazonka-ml+A user-supplied name or description of the  Evaluation.  amazonka-ml0A user-supplied ID that uniquely identifies the  Evaluation.  amazonka-mlThe ID of the MLModel to evaluate. The schema used in creating the MLModel must match the schema of the  DataSource used in the  Evaluation.  amazonka-mlThe ID of the  DataSource( for the evaluation. The schema of the  DataSource* must match the schema used to create the MLModel.  amazonka-mlCreate a value of  " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility: ,  5 - The user-supplied ID that uniquely identifies the  Evaluation6. This value should be identical to the value of the  EvaluationId in the request. ,  # - The response's http status code.  amazonka-ml2The user-supplied ID that uniquely identifies the  Evaluation6. This value should be identical to the value of the  EvaluationId in the request.  amazonka-ml The response's http status code.  amazonka-ml  amazonka-ml  amazonka-ml  amazonka-ml   1(c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred "%&';f  amazonka-mlRepresents the output of a CreateDataSourceFromS3 operation, and is an acknowledgement that Amazon ML received the request.The CreateDataSourceFromS3 operation is asynchronous. You can poll for updates by using the GetBatchPrediction operation and checking the Status parameter.See:   smart constructor.  amazonka-ml0A user-supplied ID that uniquely identifies the  DataSource6. This value should be identical to the value of the  DataSourceID in the request.  amazonka-ml The response's http status code.  amazonka-mlSee:   smart constructor.  amazonka-mlThe compute statistics for a  DataSource. The statistics are generated from the observation data referenced by a  DataSource3. Amazon ML uses the statistics internally during MLModel* training. This parameter must be set to true( if the DataSource needs to be used for MLModel training.  amazonka-ml+A user-supplied name or description of the  DataSource.  amazonka-ml8A user-supplied identifier that uniquely identifies the  DataSource.  amazonka-mlThe data specification of a  DataSource:DataLocationS3 - The Amazon S3 location of the observation data.5DataSchemaLocationS3 - The Amazon S3 location of the  DataSchema.DataSchema - A JSON string representing the schema. This is not required if  DataSchemaUri is specified.DataRearrangement - A JSON string that represents the splitting and rearrangement requirements for the  Datasource. Sample - : "{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"  amazonka-mlCreate a value of  " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility: ,   - The compute statistics for a  DataSource. The statistics are generated from the observation data referenced by a  DataSource3. Amazon ML uses the statistics internally during MLModel* training. This parameter must be set to true( if the DataSource needs to be used for MLModel training. ,  . - A user-supplied name or description of the  DataSource. ,  ; - A user-supplied identifier that uniquely identifies the  DataSource. ,   - The data specification of a  DataSource:DataLocationS3 - The Amazon S3 location of the observation data.5DataSchemaLocationS3 - The Amazon S3 location of the  DataSchema.DataSchema - A JSON string representing the schema. This is not required if  DataSchemaUri is specified.DataRearrangement - A JSON string that represents the splitting and rearrangement requirements for the  Datasource. Sample - : "{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"  amazonka-mlThe compute statistics for a  DataSource. The statistics are generated from the observation data referenced by a  DataSource3. Amazon ML uses the statistics internally during MLModel* training. This parameter must be set to true( if the DataSource needs to be used for MLModel training.  amazonka-ml+A user-supplied name or description of the  DataSource.  amazonka-ml8A user-supplied identifier that uniquely identifies the  DataSource.  amazonka-mlThe data specification of a  DataSource:DataLocationS3 - The Amazon S3 location of the observation data.5DataSchemaLocationS3 - The Amazon S3 location of the  DataSchema.DataSchema - A JSON string representing the schema. This is not required if  DataSchemaUri is specified.DataRearrangement - A JSON string that represents the splitting and rearrangement requirements for the  Datasource. Sample - : "{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"  amazonka-mlCreate a value of  " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility: ,  3 - A user-supplied ID that uniquely identifies the  DataSource6. This value should be identical to the value of the  DataSourceID in the request. ,  # - The response's http status code.  amazonka-ml0A user-supplied ID that uniquely identifies the  DataSource6. This value should be identical to the value of the  DataSourceID in the request.  amazonka-ml The response's http status code.  amazonka-ml  amazonka-ml  amazonka-ml   2(c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred "%&';%  amazonka-mlRepresents the output of a CreateDataSourceFromRedshift operation, and is an acknowledgement that Amazon ML received the request.The CreateDataSourceFromRedshift operation is asynchronous. You can poll for updates by using the GetBatchPrediction operation and checking the Status parameter.See:   smart constructor.  amazonka-mlA user-supplied ID that uniquely identifies the datasource. This value should be identical to the value of the  DataSourceID in the request.  amazonka-ml The response's http status code.  amazonka-mlSee:   smart constructor.  amazonka-mlThe compute statistics for a  DataSource. The statistics are generated from the observation data referenced by a  DataSource3. Amazon ML uses the statistics internally during MLModel* training. This parameter must be set to true if the  DataSource needs to be used for MLModel training.  amazonka-ml+A user-supplied name or description of the  DataSource.  amazonka-ml0A user-supplied ID that uniquely identifies the  DataSource.  amazonka-ml-The data specification of an Amazon Redshift  DataSource:DatabaseInformation - DatabaseName, - The name of the Amazon Redshift database. ClusterIdentifier5 - The unique ID for the Amazon Redshift cluster.DatabaseCredentials - The AWS Identity and Access Management (IAM) credentials that are used to connect to the Amazon Redshift database.SelectSqlQuery - The query that is used to retrieve the observation data for the  Datasource.S3StagingLocation - The Amazon Simple Storage Service (Amazon S3) location for staging Amazon Redshift data. The data retrieved from Amazon Redshift using the SelectSqlQuery' query is stored in this location..DataSchemaUri - The Amazon S3 location of the  DataSchema.DataSchema - A JSON string representing the schema. This is not required if  DataSchemaUri is specified.DataRearrangement - A JSON string that represents the splitting and rearrangement requirements for the  DataSource. Sample - : "{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"  amazonka-mlA fully specified role Amazon Resource Name (ARN). Amazon ML assumes the role on behalf of the user to create the following:3A security group to allow Amazon ML to execute the SelectSqlQuery) query on an Amazon Redshift clusterAn Amazon S3 bucket policy to grant Amazon ML read/write permissions on the S3StagingLocation  amazonka-mlCreate a value of  " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility: ,   - The compute statistics for a  DataSource. The statistics are generated from the observation data referenced by a  DataSource3. Amazon ML uses the statistics internally during MLModel* training. This parameter must be set to true if the  DataSource needs to be used for MLModel training. ,  . - A user-supplied name or description of the  DataSource. ,  3 - A user-supplied ID that uniquely identifies the  DataSource. ,  0 - The data specification of an Amazon Redshift  DataSource:DatabaseInformation - DatabaseName, - The name of the Amazon Redshift database. ClusterIdentifier5 - The unique ID for the Amazon Redshift cluster.DatabaseCredentials - The AWS Identity and Access Management (IAM) credentials that are used to connect to the Amazon Redshift database.SelectSqlQuery - The query that is used to retrieve the observation data for the  Datasource.S3StagingLocation - The Amazon Simple Storage Service (Amazon S3) location for staging Amazon Redshift data. The data retrieved from Amazon Redshift using the SelectSqlQuery' query is stored in this location..DataSchemaUri - The Amazon S3 location of the  DataSchema.DataSchema - A JSON string representing the schema. This is not required if  DataSchemaUri is specified.DataRearrangement - A JSON string that represents the splitting and rearrangement requirements for the  DataSource. Sample - : "{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}" ,   - A fully specified role Amazon Resource Name (ARN). Amazon ML assumes the role on behalf of the user to create the following:3A security group to allow Amazon ML to execute the SelectSqlQuery) query on an Amazon Redshift clusterAn Amazon S3 bucket policy to grant Amazon ML read/write permissions on the S3StagingLocation  amazonka-mlThe compute statistics for a  DataSource. The statistics are generated from the observation data referenced by a  DataSource3. Amazon ML uses the statistics internally during MLModel* training. This parameter must be set to true if the  DataSource needs to be used for MLModel training.  amazonka-ml+A user-supplied name or description of the  DataSource.  amazonka-ml0A user-supplied ID that uniquely identifies the  DataSource.  amazonka-ml-The data specification of an Amazon Redshift  DataSource:DatabaseInformation - DatabaseName, - The name of the Amazon Redshift database. ClusterIdentifier5 - The unique ID for the Amazon Redshift cluster.DatabaseCredentials - The AWS Identity and Access Management (IAM) credentials that are used to connect to the Amazon Redshift database.SelectSqlQuery - The query that is used to retrieve the observation data for the  Datasource.S3StagingLocation - The Amazon Simple Storage Service (Amazon S3) location for staging Amazon Redshift data. The data retrieved from Amazon Redshift using the SelectSqlQuery' query is stored in this location..DataSchemaUri - The Amazon S3 location of the  DataSchema.DataSchema - A JSON string representing the schema. This is not required if  DataSchemaUri is specified.DataRearrangement - A JSON string that represents the splitting and rearrangement requirements for the  DataSource. Sample - : "{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"  amazonka-mlA fully specified role Amazon Resource Name (ARN). Amazon ML assumes the role on behalf of the user to create the following:3A security group to allow Amazon ML to execute the SelectSqlQuery) query on an Amazon Redshift clusterAn Amazon S3 bucket policy to grant Amazon ML read/write permissions on the S3StagingLocation  amazonka-mlCreate a value of  " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility: ,   - A user-supplied ID that uniquely identifies the datasource. This value should be identical to the value of the  DataSourceID in the request. ,  # - The response's http status code.  amazonka-mlA user-supplied ID that uniquely identifies the datasource. This value should be identical to the value of the  DataSourceID in the request.  amazonka-ml The response's http status code.  amazonka-ml  amazonka-ml  amazonka-ml  amazonka-ml   3(c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred "%&';P  amazonka-mlRepresents the output of a CreateDataSourceFromRDS operation, and is an acknowledgement that Amazon ML received the request.The CreateDataSourceFromRDS> operation is asynchronous. You can poll for updates by using the GetBatchPrediction operation and checking the Status parameter. You can inspect the Message when Status shows up as FAILED. You can also check the progress of the copy operation by going to the  DataPipeline0 console and looking up the pipeline using the  pipelineId  from the describe call.See:   smart constructor.  amazonka-mlA user-supplied ID that uniquely identifies the datasource. This value should be identical to the value of the  DataSourceID in the request.  amazonka-ml The response's http status code.  amazonka-mlSee:   smart constructor.  amazonka-mlThe compute statistics for a  DataSource. The statistics are generated from the observation data referenced by a  DataSource3. Amazon ML uses the statistics internally during MLModel* training. This parameter must be set to true( if the DataSource needs to be used for MLModel training.  amazonka-ml+A user-supplied name or description of the  DataSource.  amazonka-ml0A user-supplied ID that uniquely identifies the  DataSource. Typically, an Amazon Resource Number (ARN) becomes the ID for a  DataSource.  amazonka-ml(The data specification of an Amazon RDS  DataSource: DatabaseInformation - DatabaseName' - The name of the Amazon RDS database.InstanceIdentifier  - A unique identifier for the Amazon RDS database instance.DatabaseCredentials - AWS Identity and Access Management (IAM) credentials that are used to connect to the Amazon RDS database.ResourceRole - A role (DataPipelineDefaultResourceRole) assumed by an EC2 instance to carry out the copy task from Amazon RDS to Amazon Simple Storage Service (Amazon S3). For more information, see  https://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.htmlRole templates for data pipelines.ServiceRole - A role (DataPipelineDefaultRole) assumed by the AWS Data Pipeline service to monitor the progress of the copy task from Amazon RDS to Amazon S3. For more information, see  https://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.htmlRole templates for data pipelines.SecurityInfo - The security information to use to access an RDS DB instance. You need to set up appropriate ingress rules for the security entity IDs provided to allow access to the Amazon RDS instance. Specify a [SubnetId, SecurityGroupIds,] pair for a VPC-based RDS DB instance.SelectSqlQuery - A query that is used to retrieve the observation data for the  Datasource.S3StagingLocation - The Amazon S3 location for staging Amazon RDS data. The data retrieved from Amazon RDS using SelectSqlQuery! is stored in this location..DataSchemaUri - The Amazon S3 location of the  DataSchema.DataSchema - A JSON string representing the schema. This is not required if  DataSchemaUri is specified.DataRearrangement - A JSON string that represents the splitting and rearrangement requirements for the  Datasource. Sample - : "{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"  amazonka-mlThe role that Amazon ML assumes on behalf of the user to create and activate a data pipeline in the user's account and copy data using the SelectSqlQuery$ query from Amazon RDS to Amazon S3.  amazonka-mlCreate a value of  " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility: ,   - The compute statistics for a  DataSource. The statistics are generated from the observation data referenced by a  DataSource3. Amazon ML uses the statistics internally during MLModel* training. This parameter must be set to true( if the DataSource needs to be used for MLModel training. ,  . - A user-supplied name or description of the  DataSource. ,  3 - A user-supplied ID that uniquely identifies the  DataSource. Typically, an Amazon Resource Number (ARN) becomes the ID for a  DataSource. ,  + - The data specification of an Amazon RDS  DataSource: DatabaseInformation - DatabaseName' - The name of the Amazon RDS database.InstanceIdentifier  - A unique identifier for the Amazon RDS database instance.DatabaseCredentials - AWS Identity and Access Management (IAM) credentials that are used to connect to the Amazon RDS database.ResourceRole - A role (DataPipelineDefaultResourceRole) assumed by an EC2 instance to carry out the copy task from Amazon RDS to Amazon Simple Storage Service (Amazon S3). For more information, see  https://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.htmlRole templates for data pipelines.ServiceRole - A role (DataPipelineDefaultRole) assumed by the AWS Data Pipeline service to monitor the progress of the copy task from Amazon RDS to Amazon S3. For more information, see  https://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.htmlRole templates for data pipelines.SecurityInfo - The security information to use to access an RDS DB instance. You need to set up appropriate ingress rules for the security entity IDs provided to allow access to the Amazon RDS instance. Specify a [SubnetId, SecurityGroupIds,] pair for a VPC-based RDS DB instance.SelectSqlQuery - A query that is used to retrieve the observation data for the  Datasource.S3StagingLocation - The Amazon S3 location for staging Amazon RDS data. The data retrieved from Amazon RDS using SelectSqlQuery! is stored in this location..DataSchemaUri - The Amazon S3 location of the  DataSchema.DataSchema - A JSON string representing the schema. This is not required if  DataSchemaUri is specified.DataRearrangement - A JSON string that represents the splitting and rearrangement requirements for the  Datasource. Sample - : "{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}" ,   - The role that Amazon ML assumes on behalf of the user to create and activate a data pipeline in the user's account and copy data using the SelectSqlQuery$ query from Amazon RDS to Amazon S3.  amazonka-mlThe compute statistics for a  DataSource. The statistics are generated from the observation data referenced by a  DataSource3. Amazon ML uses the statistics internally during MLModel* training. This parameter must be set to true( if the DataSource needs to be used for MLModel training.  amazonka-ml+A user-supplied name or description of the  DataSource.  amazonka-ml0A user-supplied ID that uniquely identifies the  DataSource. Typically, an Amazon Resource Number (ARN) becomes the ID for a  DataSource.  amazonka-ml(The data specification of an Amazon RDS  DataSource: DatabaseInformation - DatabaseName' - The name of the Amazon RDS database.InstanceIdentifier  - A unique identifier for the Amazon RDS database instance.DatabaseCredentials - AWS Identity and Access Management (IAM) credentials that are used to connect to the Amazon RDS database.ResourceRole - A role (DataPipelineDefaultResourceRole) assumed by an EC2 instance to carry out the copy task from Amazon RDS to Amazon Simple Storage Service (Amazon S3). For more information, see  https://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.htmlRole templates for data pipelines.ServiceRole - A role (DataPipelineDefaultRole) assumed by the AWS Data Pipeline service to monitor the progress of the copy task from Amazon RDS to Amazon S3. For more information, see  https://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.htmlRole templates for data pipelines.SecurityInfo - The security information to use to access an RDS DB instance. You need to set up appropriate ingress rules for the security entity IDs provided to allow access to the Amazon RDS instance. Specify a [SubnetId, SecurityGroupIds,] pair for a VPC-based RDS DB instance.SelectSqlQuery - A query that is used to retrieve the observation data for the  Datasource.S3StagingLocation - The Amazon S3 location for staging Amazon RDS data. The data retrieved from Amazon RDS using SelectSqlQuery! is stored in this location..DataSchemaUri - The Amazon S3 location of the  DataSchema.DataSchema - A JSON string representing the schema. This is not required if  DataSchemaUri is specified.DataRearrangement - A JSON string that represents the splitting and rearrangement requirements for the  Datasource. Sample - : "{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"  amazonka-mlThe role that Amazon ML assumes on behalf of the user to create and activate a data pipeline in the user's account and copy data using the SelectSqlQuery$ query from Amazon RDS to Amazon S3.  amazonka-mlCreate a value of  " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility: ,   - A user-supplied ID that uniquely identifies the datasource. This value should be identical to the value of the  DataSourceID in the request. ,  # - The response's http status code.  amazonka-mlA user-supplied ID that uniquely identifies the datasource. This value should be identical to the value of the  DataSourceID in the request.  amazonka-ml The response's http status code.  amazonka-ml  amazonka-ml  amazonka-ml  amazonka-ml   4(c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred "%&';d  amazonka-mlRepresents the output of a CreateBatchPrediction operation, and is an acknowledgement that Amazon ML received the request.The CreateBatchPrediction operation is asynchronous. You can poll for status updates by using the >GetBatchPrediction operation and checking the Status parameter of the result.See:   smart constructor.  amazonka-ml0A user-supplied ID that uniquely identifies the BatchPrediction/. This value is identical to the value of the BatchPredictionId in the request.  amazonka-ml The response's http status code.  amazonka-mlSee:   smart constructor.  amazonka-ml+A user-supplied name or description of the BatchPrediction. BatchPredictionName& can only use the UTF-8 character set.  amazonka-ml0A user-supplied ID that uniquely identifies the BatchPrediction.  amazonka-mlThe ID of the MLModel? that will generate predictions for the group of observations.  amazonka-mlThe ID of the  DataSource6 that points to the group of observations to predict.  amazonka-mlThe location of an Amazon Simple Storage Service (Amazon S3) bucket or directory to store the batch prediction results. The following substrings are not allowed in the s3 key portion of the  outputURI" field: ':', '//', '/./', '/../'.Amazon ML needs permissions to store and retrieve the logs on your behalf. For information about how to set permissions, see the  6https://docs.aws.amazon.com/machine-learning/latest/dg'Amazon Machine Learning Developer Guide.  amazonka-mlCreate a value of  " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility: ,  . - A user-supplied name or description of the BatchPrediction. BatchPredictionName& can only use the UTF-8 character set. ,  3 - A user-supplied ID that uniquely identifies the BatchPrediction. ,   - The ID of the MLModel? that will generate predictions for the group of observations. ,   - The ID of the  DataSource6 that points to the group of observations to predict. ,   - The location of an Amazon Simple Storage Service (Amazon S3) bucket or directory to store the batch prediction results. The following substrings are not allowed in the s3 key portion of the  outputURI" field: ':', '//', '/./', '/../'.Amazon ML needs permissions to store and retrieve the logs on your behalf. For information about how to set permissions, see the  6https://docs.aws.amazon.com/machine-learning/latest/dg'Amazon Machine Learning Developer Guide.  amazonka-ml+A user-supplied name or description of the BatchPrediction. BatchPredictionName& can only use the UTF-8 character set.  amazonka-ml0A user-supplied ID that uniquely identifies the BatchPrediction.  amazonka-mlThe ID of the MLModel? that will generate predictions for the group of observations.  amazonka-mlThe ID of the  DataSource6 that points to the group of observations to predict.  amazonka-mlThe location of an Amazon Simple Storage Service (Amazon S3) bucket or directory to store the batch prediction results. The following substrings are not allowed in the s3 key portion of the  outputURI" field: ':', '//', '/./', '/../'.Amazon ML needs permissions to store and retrieve the logs on your behalf. For information about how to set permissions, see the  6https://docs.aws.amazon.com/machine-learning/latest/dg'Amazon Machine Learning Developer Guide.  amazonka-mlCreate a value of  " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility: ,  3 - A user-supplied ID that uniquely identifies the BatchPrediction/. This value is identical to the value of the BatchPredictionId in the request. ,  # - The response's http status code.  amazonka-ml0A user-supplied ID that uniquely identifies the BatchPrediction/. This value is identical to the value of the BatchPredictionId in the request.  amazonka-ml The response's http status code.  amazonka-ml  amazonka-ml  amazonka-ml  amazonka-ml  amazonka-ml   5(c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred "%&';o5  amazonka-ml)Amazon ML returns the following elements.See:   smart constructor.  amazonka-ml(The ID of the ML object that was tagged.  amazonka-ml*The type of the ML object that was tagged.  amazonka-ml The response's http status code.  amazonka-mlSee:   smart constructor.  amazonka-mlThe key-value pairs to use to create tags. If you specify a key without specifying a value, Amazon ML creates a tag with the specified key and a value of null.  amazonka-ml-The ID of the ML object to tag. For example, exampleModelId.  amazonka-ml!The type of the ML object to tag.  amazonka-mlCreate a value of  " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility: ,   - The key-value pairs to use to create tags. If you specify a key without specifying a value, Amazon ML creates a tag with the specified key and a value of null. ,  0 - The ID of the ML object to tag. For example, exampleModelId. ,  $ - The type of the ML object to tag.  amazonka-mlThe key-value pairs to use to create tags. If you specify a key without specifying a value, Amazon ML creates a tag with the specified key and a value of null.  amazonka-ml-The ID of the ML object to tag. For example, exampleModelId.  amazonka-ml!The type of the ML object to tag.  amazonka-mlCreate a value of  " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility: ,  + - The ID of the ML object that was tagged. ,  - - The type of the ML object that was tagged. ,  # - The response's http status code.  amazonka-ml(The ID of the ML object that was tagged.  amazonka-ml*The type of the ML object that was tagged.  amazonka-ml The response's http status code.  amazonka-ml  amazonka-ml  amazonka-ml   6(c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred "%&';y  amazonka-mlRepresents the output of an UpdateBatchPrediction operation.-You can see the updated content by using the GetBatchPrediction operation.See:   smart constructor.  amazonka-mlThe ID assigned to the BatchPrediction during creation. This value should be identical to the value of the BatchPredictionId in the request.  amazonka-ml The response's http status code.  amazonka-mlSee:   smart constructor.  amazonka-mlThe ID assigned to the BatchPrediction during creation.  amazonka-ml/A new user-supplied name or description of the BatchPrediction.  amazonka-mlCreate a value of  " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility: ,   - The ID assigned to the BatchPrediction during creation. ,  2 - A new user-supplied name or description of the BatchPrediction.  amazonka-mlThe ID assigned to the BatchPrediction during creation.  amazonka-ml/A new user-supplied name or description of the BatchPrediction.  amazonka-mlCreate a value of  " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility: ,   - The ID assigned to the BatchPrediction during creation. This value should be identical to the value of the BatchPredictionId in the request. ,  # - The response's http status code.  amazonka-mlThe ID assigned to the BatchPrediction during creation. This value should be identical to the value of the BatchPredictionId in the request.  amazonka-ml The response's http status code.  amazonka-ml  amazonka-ml  amazonka-ml   7(c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred "%&';  amazonka-mlRepresents the output of an UpdateDataSource operation.-You can see the updated content by using the GetBatchPrediction operation.See:   smart constructor.  amazonka-mlThe ID assigned to the  DataSource during creation. This value should be identical to the value of the  DataSourceID in the request.  amazonka-ml The response's http status code.  amazonka-mlSee:   smart constructor.  amazonka-mlThe ID assigned to the  DataSource during creation.  amazonka-ml/A new user-supplied name or description of the  DataSource, that will replace the current description.  amazonka-mlCreate a value of  " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility: ,   - The ID assigned to the  DataSource during creation. ,  2 - A new user-supplied name or description of the  DataSource, that will replace the current description.  amazonka-mlThe ID assigned to the  DataSource during creation.  amazonka-ml/A new user-supplied name or description of the  DataSource, that will replace the current description.  amazonka-mlCreate a value of  " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility: ,   - The ID assigned to the  DataSource during creation. This value should be identical to the value of the  DataSourceID in the request. ,  # - The response's http status code.  amazonka-mlThe ID assigned to the  DataSource during creation. This value should be identical to the value of the  DataSourceID in the request.  amazonka-ml The response's http status code.  amazonka-ml  amazonka-ml  amazonka-ml   8(c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred "%&';  amazonka-mlRepresents the output of an UpdateEvaluation operation.-You can see the updated content by using the  GetEvaluation operation.See:   smart constructor.  amazonka-mlThe ID assigned to the  Evaluation during creation. This value should be identical to the value of the  Evaluation in the request.  amazonka-ml The response's http status code.  amazonka-mlSee:   smart constructor.  amazonka-mlThe ID assigned to the  Evaluation during creation.  amazonka-ml/A new user-supplied name or description of the  Evaluation( that will replace the current content.  amazonka-mlCreate a value of  " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility: ,   - The ID assigned to the  Evaluation during creation. ,  2 - A new user-supplied name or description of the  Evaluation( that will replace the current content.  amazonka-mlThe ID assigned to the  Evaluation during creation.  amazonka-ml/A new user-supplied name or description of the  Evaluation( that will replace the current content.  amazonka-mlCreate a value of  " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility: ,   - The ID assigned to the  Evaluation during creation. This value should be identical to the value of the  Evaluation in the request. ,  # - The response's http status code.  amazonka-mlThe ID assigned to the  Evaluation during creation. This value should be identical to the value of the  Evaluation in the request.  amazonka-ml The response's http status code.  amazonka-ml  amazonka-ml  amazonka-ml   9(c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred "%&';.  amazonka-mlRepresents the output of an  UpdateMLModel operation.-You can see the updated content by using the  GetMLModel operation.See:  smart constructor.  amazonka-mlThe ID assigned to the MLModel during creation. This value should be identical to the value of the  MLModelID in the request.  amazonka-ml The response's http status code.  amazonka-mlSee:   smart constructor.  amazonka-ml+A user-supplied name or description of the MLModel.  amazonka-mlThe ScoreThreshold used in binary classification MLModel that marks the boundary between a positive prediction and a negative prediction.+Output values greater than or equal to the ScoreThreshold% receive a positive result from the MLModel , such as true. Output values less than the ScoreThreshold' receive a negative response from the MLModel , such as false.  amazonka-mlThe ID assigned to the MLModel during creation.  amazonka-mlCreate a value of  " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility: ,  . - A user-supplied name or description of the MLModel. ,  - The ScoreThreshold used in binary classification MLModel that marks the boundary between a positive prediction and a negative prediction.+Output values greater than or equal to the ScoreThreshold% receive a positive result from the MLModel , such as true. Output values less than the ScoreThreshold' receive a negative response from the MLModel , such as false. ,  - The ID assigned to the MLModel during creation.  amazonka-ml+A user-supplied name or description of the MLModel. amazonka-mlThe ScoreThreshold used in binary classification MLModel that marks the boundary between a positive prediction and a negative prediction.+Output values greater than or equal to the ScoreThreshold% receive a positive result from the MLModel , such as true. Output values less than the ScoreThreshold' receive a negative response from the MLModel , such as false. amazonka-mlThe ID assigned to the MLModel during creation. amazonka-mlCreate a value of  " with all optional fields omitted.Use  0https://hackage.haskell.org/package/generic-lens generic-lens or  *https://hackage.haskell.org/package/opticsoptics! to modify other optional fields.The following record fields are available, with the corresponding lenses provided for backwards compatibility: ,  - The ID assigned to the MLModel during creation. This value should be identical to the value of the  MLModelID in the request. , # - The response's http status code. amazonka-mlThe ID assigned to the MLModel during creation. This value should be identical to the value of the  MLModelID in the request. amazonka-ml The response's http status code.  amazonka-ml  amazonka-ml    ;(c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred   :(c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred"%\ amazonka-mlPolls <= every 30 seconds until a successful state is reached. An error is returned after 60 failed checks. amazonka-mlPolls <> every 30 seconds until a successful state is reached. An error is returned after 60 failed checks. amazonka-mlPolls <? every 30 seconds until a successful state is reached. An error is returned after 60 failed checks. amazonka-mlPolls <@ every 30 seconds until a successful state is reached. An error is returned after 60 failed checks.<(c) 2013-2023 Brendan HayMozilla Public License, v. 2.0. Brendan Hayauto-generatednon-portable (GHC extensions) Safe-Inferred! 5=<;:9867QUTRSiponmljk    ! ! 5=<;:9867=<;:98QUTRSUTiponmljkponmlABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~                                                                                                                                                                    !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""##################################$$$$$$@$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$%%%%%%?%% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % & & & & & &>& & & & & & & & & & & & & & & & & & & & & & & & & & & & & & & & & & & & & & & & & & & & & ' ' ' ' ' '=' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) * * * * * * * * * * * * * * * * * * * * * * * * * * * * + + + + + + + + + + + + + + + + + + + + + + + + + + + + , , , , , , , , , , , , , , , , , , , , , , , , , , , , - - - - - - - - - - - - - - - - - - - - - - - - - - - - . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 7 7 7 7 7 7 7 7 7 7 7777777777777777777788888888888888888888888888888899999999999999999999999999999999::::&amazonka-ml-2.0-A3JLJ63WvmfHxGBBIqhdRA(Amazonka.MachineLearning.Types.Algorithm