Amazonka.MachineLearning.CreateMLModel

Creates a new MLModel using the DataSource and the recipe as information sources.

An MLModel is nearly immutable. Users can update only the MLModelName and the ScoreThreshold in an MLModel without creating a new MLModel.

CreateMLModel is an asynchronous operation. In response to CreateMLModel, Amazon Machine Learning (Amazon ML) immediately returns and sets the MLModel status to PENDING. After the MLModel has been created and ready is for use, Amazon ML sets the status to COMPLETED.

You can use the GetMLModel operation to check the progress of the MLModel during the creation operation.

CreateMLModel requires a DataSource with computed statistics, which can be created by setting ComputeStatistics to true in CreateDataSourceFromRDS, CreateDataSourceFromS3, or CreateDataSourceFromRedshift operations.

Creating a Request

data CreateMLModel

See: newCreateMLModel smart constructor.

Constructors

CreateMLModel'

Fields

• mLModelName :: Maybe Text

  A user-supplied name or description of the MLModel.

• parameters :: Maybe (HashMap Text Text)

  A list of the training parameters in the MLModel. The list is implemented as a map of key-value pairs.

  The 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 MLModel. 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. 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.

• recipe :: Maybe Text

  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.

• recipeUri :: Maybe Text

  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.

• mLModelId :: Text

  A user-supplied ID that uniquely identifies the MLModel.

• mLModelType :: MLModelType

  The category of supervised learning that this MLModel 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 Amazon Machine Learning Developer Guide.

• trainingDataSourceId :: Text

  The DataSource that points to the training data.

Instances

ToJSON CreateMLModel

Defined in Amazonka.MachineLearning.CreateMLModel

Methods

toJSON :: CreateMLModel -> Value

toEncoding :: CreateMLModel -> Encoding

toJSONList :: [CreateMLModel] -> Value

toEncodingList :: [CreateMLModel] -> Encoding

ToHeaders CreateMLModel

Defined in Amazonka.MachineLearning.CreateMLModel

Methods

toHeaders :: CreateMLModel -> [Header]

ToPath CreateMLModel

Defined in Amazonka.MachineLearning.CreateMLModel

Methods

toPath :: CreateMLModel -> ByteString

ToQuery CreateMLModel

Defined in Amazonka.MachineLearning.CreateMLModel

Methods

toQuery :: CreateMLModel -> QueryString

AWSRequest CreateMLModel

Defined in Amazonka.MachineLearning.CreateMLModel

Associated Types

type AWSResponse CreateMLModel

Methods

request :: (Service -> Service) -> CreateMLModel -> Request CreateMLModel

response :: MonadResource m => (ByteStringLazy -> IO ByteStringLazy) -> Service -> Proxy CreateMLModel -> ClientResponse ClientBody -> m (Either Error (ClientResponse (AWSResponse CreateMLModel)))

Generic CreateMLModel

Defined in Amazonka.MachineLearning.CreateMLModel

Associated Types

type Rep CreateMLModel :: Type -> Type

Methods

from :: CreateMLModel -> Rep CreateMLModel x

to :: Rep CreateMLModel x -> CreateMLModel

Read CreateMLModel

Defined in Amazonka.MachineLearning.CreateMLModel

Methods

readsPrec :: Int -> ReadS CreateMLModel

readList :: ReadS [CreateMLModel]

readPrec :: ReadPrec CreateMLModel

readListPrec :: ReadPrec [CreateMLModel]

Show CreateMLModel

Defined in Amazonka.MachineLearning.CreateMLModel

Methods

showsPrec :: Int -> CreateMLModel -> ShowS

show :: CreateMLModel -> String

showList :: [CreateMLModel] -> ShowS

NFData CreateMLModel

Defined in Amazonka.MachineLearning.CreateMLModel

Methods

rnf :: CreateMLModel -> ()

Eq CreateMLModel

Defined in Amazonka.MachineLearning.CreateMLModel

Methods

(==) :: CreateMLModel -> CreateMLModel -> Bool

(/=) :: CreateMLModel -> CreateMLModel -> Bool

Hashable CreateMLModel

Defined in Amazonka.MachineLearning.CreateMLModel

Methods

hashWithSalt :: Int -> CreateMLModel -> Int

hash :: CreateMLModel -> Int

type AWSResponse CreateMLModel

Defined in Amazonka.MachineLearning.CreateMLModel

type AWSResponse CreateMLModel = CreateMLModelResponse

type Rep CreateMLModel

Defined in Amazonka.MachineLearning.CreateMLModel

type Rep CreateMLModel = D1 ('MetaData "CreateMLModel" "Amazonka.MachineLearning.CreateMLModel" "amazonka-ml-2.0-A3JLJ63WvmfHxGBBIqhdRA" 'False) (C1 ('MetaCons "CreateMLModel'" 'PrefixI 'True) ((S1 ('MetaSel ('Just "mLModelName") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :*: (S1 ('MetaSel ('Just "parameters") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe (HashMap Text Text))) :*: S1 ('MetaSel ('Just "recipe") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)))) :*: ((S1 ('MetaSel ('Just "recipeUri") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :*: S1 ('MetaSel ('Just "mLModelId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text)) :*: (S1 ('MetaSel ('Just "mLModelType") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 MLModelType) :*: S1 ('MetaSel ('Just "trainingDataSourceId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text)))))

newCreateMLModel

Arguments

:: Text

CreateMLModel

-> MLModelType

CreateMLModel

-> Text

CreateMLModel

-> CreateMLModel

Create a value of CreateMLModel with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:mLModelName:CreateMLModel', createMLModel_mLModelName - A user-supplied name or description of the MLModel.

$sel:parameters:CreateMLModel', createMLModel_parameters - A list of the training parameters in the MLModel. The list is implemented as a map of key-value pairs.

The 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 MLModel. 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. 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.

$sel:recipe:CreateMLModel', createMLModel_recipe - 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.

$sel:recipeUri:CreateMLModel', createMLModel_recipeUri - 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.

CreateMLModel, createMLModel_mLModelId - A user-supplied ID that uniquely identifies the MLModel.

CreateMLModel, createMLModel_mLModelType - The category of supervised learning that this MLModel 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 Amazon Machine Learning Developer Guide.

CreateMLModel, createMLModel_trainingDataSourceId - The DataSource that points to the training data.

Request Lenses

createMLModel_mLModelName :: Lens' CreateMLModel (Maybe Text)

A user-supplied name or description of the MLModel.

createMLModel_parameters :: Lens' CreateMLModel (Maybe (HashMap Text Text))

A list of the training parameters in the MLModel. The list is implemented as a map of key-value pairs.

The 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 MLModel. 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. 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.

createMLModel_recipe :: Lens' CreateMLModel (Maybe Text)

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.

createMLModel_recipeUri :: Lens' CreateMLModel (Maybe Text)

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.

createMLModel_mLModelId :: Lens' CreateMLModel Text

A user-supplied ID that uniquely identifies the MLModel.

createMLModel_mLModelType :: Lens' CreateMLModel MLModelType

The category of supervised learning that this MLModel 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 Amazon Machine Learning Developer Guide.

createMLModel_trainingDataSourceId :: Lens' CreateMLModel Text

The DataSource that points to the training data.

Destructuring the Response

data CreateMLModelResponse

Represents 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: newCreateMLModelResponse smart constructor.

Constructors

CreateMLModelResponse'

Fields

• mLModelId :: Maybe Text

  A user-supplied ID that uniquely identifies the MLModel. This value should be identical to the value of the MLModelId in the request.

• httpStatus :: Int

  The response's http status code.

Instances

Generic CreateMLModelResponse

Defined in Amazonka.MachineLearning.CreateMLModel

Associated Types

type Rep CreateMLModelResponse :: Type -> Type

Methods

from :: CreateMLModelResponse -> Rep CreateMLModelResponse x

to :: Rep CreateMLModelResponse x -> CreateMLModelResponse

Read CreateMLModelResponse

Defined in Amazonka.MachineLearning.CreateMLModel

Methods

readsPrec :: Int -> ReadS CreateMLModelResponse

readList :: ReadS [CreateMLModelResponse]

readPrec :: ReadPrec CreateMLModelResponse

readListPrec :: ReadPrec [CreateMLModelResponse]

Show CreateMLModelResponse

Defined in Amazonka.MachineLearning.CreateMLModel

Methods

showsPrec :: Int -> CreateMLModelResponse -> ShowS

show :: CreateMLModelResponse -> String

showList :: [CreateMLModelResponse] -> ShowS

NFData CreateMLModelResponse

Defined in Amazonka.MachineLearning.CreateMLModel

Methods

rnf :: CreateMLModelResponse -> ()

Eq CreateMLModelResponse

Defined in Amazonka.MachineLearning.CreateMLModel

Methods

(==) :: CreateMLModelResponse -> CreateMLModelResponse -> Bool

(/=) :: CreateMLModelResponse -> CreateMLModelResponse -> Bool

type Rep CreateMLModelResponse

Defined in Amazonka.MachineLearning.CreateMLModel

type Rep CreateMLModelResponse = D1 ('MetaData "CreateMLModelResponse" "Amazonka.MachineLearning.CreateMLModel" "amazonka-ml-2.0-A3JLJ63WvmfHxGBBIqhdRA" 'False) (C1 ('MetaCons "CreateMLModelResponse'" 'PrefixI 'True) (S1 ('MetaSel ('Just "mLModelId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :*: S1 ('MetaSel ('Just "httpStatus") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Int)))

newCreateMLModelResponse

Arguments

:: Int

$sel:httpStatus:CreateMLModelResponse'

-> CreateMLModelResponse

Create a value of CreateMLModelResponse with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

CreateMLModel, createMLModelResponse_mLModelId - A user-supplied ID that uniquely identifies the MLModel. This value should be identical to the value of the MLModelId in the request.

$sel:httpStatus:CreateMLModelResponse', createMLModelResponse_httpStatus - The response's http status code.

Response Lenses

createMLModelResponse_mLModelId :: Lens' CreateMLModelResponse (Maybe Text)

A user-supplied ID that uniquely identifies the MLModel. This value should be identical to the value of the MLModelId in the request.

createMLModelResponse_httpStatus :: Lens' CreateMLModelResponse Int

The response's http status code.