amazonka-ml-1.4.3: Amazon Machine Learning SDK.

Copyright(c) 2013-2016 Brendan Hay
LicenseMozilla Public License, v. 2.0.
MaintainerBrendan Hay <brendan.g.hay@gmail.com>
Stabilityauto-generated
Portabilitynon-portable (GHC extensions)
Safe HaskellNone
LanguageHaskell2010

Network.AWS.MachineLearning

Contents

Description

Definition of the public APIs exposed by Amazon Machine Learning

Synopsis

Service Configuration

machineLearning :: Service Source #

API version '2014-12-12' of the Amazon Machine Learning SDK configuration.

Errors

Error matchers are designed for use with the functions provided by Control.Exception.Lens. This allows catching (and rethrowing) service specific errors returned by MachineLearning.

InternalServerException

_InternalServerException :: AsError a => Getting (First ServiceError) a ServiceError Source #

An error on the server occurred when trying to process a request.

InvalidInputException

_InvalidInputException :: AsError a => Getting (First ServiceError) a ServiceError Source #

An error on the client occurred. Typically, the cause is an invalid input value.

IdempotentParameterMismatchException

_IdempotentParameterMismatchException :: AsError a => Getting (First ServiceError) a ServiceError Source #

A 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.

PredictorNotMountedException

_PredictorNotMountedException :: AsError a => Getting (First ServiceError) a ServiceError Source #

The exception is thrown when a predict request is made to an unmounted MLModel.

ResourceNotFoundException

_ResourceNotFoundException :: AsError a => Getting (First ServiceError) a ServiceError Source #

A specified resource cannot be located.

LimitExceededException

_LimitExceededException :: AsError a => Getting (First ServiceError) a ServiceError Source #

The subscriber exceeded the maximum number of operations. This exception can occur when listing objects such as DataSource.

Waiters

Waiters poll by repeatedly sending a request until some remote success condition configured by the Wait specification is fulfilled. The Wait specification determines how many attempts should be made, in addition to delay and retry strategies.

MLModelAvailable

mLModelAvailable :: Wait DescribeMLModels Source #

Polls DescribeMLModels every 30 seconds until a successful state is reached. An error is returned after 60 failed checks.

BatchPredictionAvailable

batchPredictionAvailable :: Wait DescribeBatchPredictions Source #

Polls DescribeBatchPredictions every 30 seconds until a successful state is reached. An error is returned after 60 failed checks.

DataSourceAvailable

dataSourceAvailable :: Wait DescribeDataSources Source #

Polls DescribeDataSources every 30 seconds until a successful state is reached. An error is returned after 60 failed checks.

EvaluationAvailable

evaluationAvailable :: Wait DescribeEvaluations Source #

Polls DescribeEvaluations every 30 seconds until a successful state is reached. An error is returned after 60 failed checks.

Operations

Some AWS operations return results that are incomplete and require subsequent requests in order to obtain the entire result set. The process of sending subsequent requests to continue where a previous request left off is called pagination. For example, the ListObjects operation of Amazon S3 returns up to 1000 objects at a time, and you must send subsequent requests with the appropriate Marker in order to retrieve the next page of results.

Operations that have an AWSPager instance can transparently perform subsequent requests, correctly setting Markers and other request facets to iterate through the entire result set of a truncated API operation. Operations which support this have an additional note in the documentation.

Many operations have the ability to filter results on the server side. See the individual operation parameters for details.

UpdateDataSource

DeleteDataSource

CreateDataSourceFromRedshift

CreateDataSourceFromS3

CreateMLModel

DeleteBatchPrediction

UpdateBatchPrediction

GetMLModel

GetDataSource

UpdateEvaluation

DeleteEvaluation

DeleteMLModel

UpdateMLModel

GetBatchPrediction

DescribeBatchPredictions (Paginated)

CreateDataSourceFromRDS

CreateEvaluation

Predict

DeleteRealtimeEndpoint

CreateBatchPrediction

GetEvaluation

DescribeEvaluations (Paginated)

CreateRealtimeEndpoint

DescribeMLModels (Paginated)

DescribeDataSources (Paginated)

Types

Algorithm

data Algorithm Source #

The function used to train a MLModel. Training choices supported by Amazon ML include the following:

  • SGD - Stochastic Gradient Descent.
  • RandomForest - Random forest of decision trees.

Constructors

SGD 

Instances

Bounded Algorithm Source # 
Enum Algorithm Source # 
Eq Algorithm Source # 
Data Algorithm Source # 

Methods

gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> Algorithm -> c Algorithm #

gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c Algorithm #

toConstr :: Algorithm -> Constr #

dataTypeOf :: Algorithm -> DataType #

dataCast1 :: Typeable (* -> *) t => (forall d. Data d => c (t d)) -> Maybe (c Algorithm) #

dataCast2 :: Typeable (* -> * -> *) t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c Algorithm) #

gmapT :: (forall b. Data b => b -> b) -> Algorithm -> Algorithm #

gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> Algorithm -> r #

gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> Algorithm -> r #

gmapQ :: (forall d. Data d => d -> u) -> Algorithm -> [u] #

gmapQi :: Int -> (forall d. Data d => d -> u) -> Algorithm -> u #

gmapM :: Monad m => (forall d. Data d => d -> m d) -> Algorithm -> m Algorithm #

gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> Algorithm -> m Algorithm #

gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> Algorithm -> m Algorithm #

Ord Algorithm Source # 
Read Algorithm Source # 
Show Algorithm Source # 
Generic Algorithm Source # 

Associated Types

type Rep Algorithm :: * -> * #

FromJSON Algorithm Source # 
Hashable Algorithm Source # 
NFData Algorithm Source # 

Methods

rnf :: Algorithm -> () #

ToHeader Algorithm Source # 
ToQuery Algorithm Source # 
ToByteString Algorithm Source # 

Methods

toBS :: Algorithm -> ByteString #

FromText Algorithm Source # 
ToText Algorithm Source # 

Methods

toText :: Algorithm -> Text #

type Rep Algorithm Source # 
type Rep Algorithm = D1 (MetaData "Algorithm" "Network.AWS.MachineLearning.Types.Sum" "amazonka-ml-1.4.3-DbYWzKGDN8K1aNJlQiNb67" False) (C1 (MetaCons "SGD" PrefixI False) U1)

BatchPredictionFilterVariable

data BatchPredictionFilterVariable Source #

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.
  • Name - 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.

Instances

Bounded BatchPredictionFilterVariable Source # 
Enum BatchPredictionFilterVariable Source # 
Eq BatchPredictionFilterVariable Source # 
Data BatchPredictionFilterVariable Source # 

Methods

gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> BatchPredictionFilterVariable -> c BatchPredictionFilterVariable #

gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c BatchPredictionFilterVariable #

toConstr :: BatchPredictionFilterVariable -> Constr #

dataTypeOf :: BatchPredictionFilterVariable -> DataType #

dataCast1 :: Typeable (* -> *) t => (forall d. Data d => c (t d)) -> Maybe (c BatchPredictionFilterVariable) #

dataCast2 :: Typeable (* -> * -> *) t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c BatchPredictionFilterVariable) #

gmapT :: (forall b. Data b => b -> b) -> BatchPredictionFilterVariable -> BatchPredictionFilterVariable #

gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> BatchPredictionFilterVariable -> r #

gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> BatchPredictionFilterVariable -> r #

gmapQ :: (forall d. Data d => d -> u) -> BatchPredictionFilterVariable -> [u] #

gmapQi :: Int -> (forall d. Data d => d -> u) -> BatchPredictionFilterVariable -> u #

gmapM :: Monad m => (forall d. Data d => d -> m d) -> BatchPredictionFilterVariable -> m BatchPredictionFilterVariable #

gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> BatchPredictionFilterVariable -> m BatchPredictionFilterVariable #

gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> BatchPredictionFilterVariable -> m BatchPredictionFilterVariable #

Ord BatchPredictionFilterVariable Source # 
Read BatchPredictionFilterVariable Source # 
Show BatchPredictionFilterVariable Source # 
Generic BatchPredictionFilterVariable Source # 
ToJSON BatchPredictionFilterVariable Source # 
Hashable BatchPredictionFilterVariable Source # 
NFData BatchPredictionFilterVariable Source # 
ToHeader BatchPredictionFilterVariable Source # 
ToQuery BatchPredictionFilterVariable Source # 
ToByteString BatchPredictionFilterVariable Source # 
FromText BatchPredictionFilterVariable Source # 
ToText BatchPredictionFilterVariable Source # 
type Rep BatchPredictionFilterVariable Source # 
type Rep BatchPredictionFilterVariable = D1 (MetaData "BatchPredictionFilterVariable" "Network.AWS.MachineLearning.Types.Sum" "amazonka-ml-1.4.3-DbYWzKGDN8K1aNJlQiNb67" False) ((:+:) ((:+:) ((:+:) (C1 (MetaCons "BatchCreatedAt" PrefixI False) U1) (C1 (MetaCons "BatchDataSourceId" PrefixI False) U1)) ((:+:) (C1 (MetaCons "BatchDataURI" PrefixI False) U1) (C1 (MetaCons "BatchIAMUser" PrefixI False) U1))) ((:+:) ((:+:) (C1 (MetaCons "BatchLastUpdatedAt" PrefixI False) U1) (C1 (MetaCons "BatchMLModelId" PrefixI False) U1)) ((:+:) (C1 (MetaCons "BatchName" PrefixI False) U1) (C1 (MetaCons "BatchStatus" PrefixI False) U1))))

DataSourceFilterVariable

data DataSourceFilterVariable Source #

A 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.

Instances

Bounded DataSourceFilterVariable Source # 
Enum DataSourceFilterVariable Source # 
Eq DataSourceFilterVariable Source # 
Data DataSourceFilterVariable Source # 

Methods

gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> DataSourceFilterVariable -> c DataSourceFilterVariable #

gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c DataSourceFilterVariable #

toConstr :: DataSourceFilterVariable -> Constr #

dataTypeOf :: DataSourceFilterVariable -> DataType #

dataCast1 :: Typeable (* -> *) t => (forall d. Data d => c (t d)) -> Maybe (c DataSourceFilterVariable) #

dataCast2 :: Typeable (* -> * -> *) t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c DataSourceFilterVariable) #

gmapT :: (forall b. Data b => b -> b) -> DataSourceFilterVariable -> DataSourceFilterVariable #

gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> DataSourceFilterVariable -> r #

gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> DataSourceFilterVariable -> r #

gmapQ :: (forall d. Data d => d -> u) -> DataSourceFilterVariable -> [u] #

gmapQi :: Int -> (forall d. Data d => d -> u) -> DataSourceFilterVariable -> u #

gmapM :: Monad m => (forall d. Data d => d -> m d) -> DataSourceFilterVariable -> m DataSourceFilterVariable #

gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> DataSourceFilterVariable -> m DataSourceFilterVariable #

gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> DataSourceFilterVariable -> m DataSourceFilterVariable #

Ord DataSourceFilterVariable Source # 
Read DataSourceFilterVariable Source # 
Show DataSourceFilterVariable Source # 
Generic DataSourceFilterVariable Source # 
ToJSON DataSourceFilterVariable Source # 
Hashable DataSourceFilterVariable Source # 
NFData DataSourceFilterVariable Source # 
ToHeader DataSourceFilterVariable Source # 
ToQuery DataSourceFilterVariable Source # 
ToByteString DataSourceFilterVariable Source # 
FromText DataSourceFilterVariable Source # 
ToText DataSourceFilterVariable Source # 
type Rep DataSourceFilterVariable Source # 
type Rep DataSourceFilterVariable = D1 (MetaData "DataSourceFilterVariable" "Network.AWS.MachineLearning.Types.Sum" "amazonka-ml-1.4.3-DbYWzKGDN8K1aNJlQiNb67" False) ((:+:) ((:+:) (C1 (MetaCons "DataCreatedAt" PrefixI False) U1) ((:+:) (C1 (MetaCons "DataDATALOCATIONS3" PrefixI False) U1) (C1 (MetaCons "DataIAMUser" PrefixI False) U1))) ((:+:) (C1 (MetaCons "DataLastUpdatedAt" PrefixI False) U1) ((:+:) (C1 (MetaCons "DataName" PrefixI False) U1) (C1 (MetaCons "DataStatus" PrefixI False) U1))))

DetailsAttributes

data DetailsAttributes Source #

Contains the key values of DetailsMap: PredictiveModelType - Indicates the type of the MLModel. Algorithm - Indicates the algorithm was used for the MLModel.

Instances

Bounded DetailsAttributes Source # 
Enum DetailsAttributes Source # 
Eq DetailsAttributes Source # 
Data DetailsAttributes Source # 

Methods

gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> DetailsAttributes -> c DetailsAttributes #

gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c DetailsAttributes #

toConstr :: DetailsAttributes -> Constr #

dataTypeOf :: DetailsAttributes -> DataType #

dataCast1 :: Typeable (* -> *) t => (forall d. Data d => c (t d)) -> Maybe (c DetailsAttributes) #

dataCast2 :: Typeable (* -> * -> *) t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c DetailsAttributes) #

gmapT :: (forall b. Data b => b -> b) -> DetailsAttributes -> DetailsAttributes #

gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> DetailsAttributes -> r #

gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> DetailsAttributes -> r #

gmapQ :: (forall d. Data d => d -> u) -> DetailsAttributes -> [u] #

gmapQi :: Int -> (forall d. Data d => d -> u) -> DetailsAttributes -> u #

gmapM :: Monad m => (forall d. Data d => d -> m d) -> DetailsAttributes -> m DetailsAttributes #

gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> DetailsAttributes -> m DetailsAttributes #

gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> DetailsAttributes -> m DetailsAttributes #

Ord DetailsAttributes Source # 
Read DetailsAttributes Source # 
Show DetailsAttributes Source # 
Generic DetailsAttributes Source # 
FromJSON DetailsAttributes Source # 
Hashable DetailsAttributes Source # 
NFData DetailsAttributes Source # 

Methods

rnf :: DetailsAttributes -> () #

ToHeader DetailsAttributes Source # 
ToQuery DetailsAttributes Source # 
ToByteString DetailsAttributes Source # 
FromText DetailsAttributes Source # 
ToText DetailsAttributes Source # 
type Rep DetailsAttributes Source # 
type Rep DetailsAttributes = D1 (MetaData "DetailsAttributes" "Network.AWS.MachineLearning.Types.Sum" "amazonka-ml-1.4.3-DbYWzKGDN8K1aNJlQiNb67" False) ((:+:) (C1 (MetaCons "Algorithm" PrefixI False) U1) (C1 (MetaCons "PredictiveModelType" PrefixI False) U1))

EntityStatus

data EntityStatus Source #

Entity status with the following possible values:

  • PENDING
  • INPROGRESS
  • FAILED
  • COMPLETED
  • DELETED

Instances

Bounded EntityStatus Source # 
Enum EntityStatus Source # 
Eq EntityStatus Source # 
Data EntityStatus Source # 

Methods

gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> EntityStatus -> c EntityStatus #

gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c EntityStatus #

toConstr :: EntityStatus -> Constr #

dataTypeOf :: EntityStatus -> DataType #

dataCast1 :: Typeable (* -> *) t => (forall d. Data d => c (t d)) -> Maybe (c EntityStatus) #

dataCast2 :: Typeable (* -> * -> *) t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c EntityStatus) #

gmapT :: (forall b. Data b => b -> b) -> EntityStatus -> EntityStatus #

gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> EntityStatus -> r #

gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> EntityStatus -> r #

gmapQ :: (forall d. Data d => d -> u) -> EntityStatus -> [u] #

gmapQi :: Int -> (forall d. Data d => d -> u) -> EntityStatus -> u #

gmapM :: Monad m => (forall d. Data d => d -> m d) -> EntityStatus -> m EntityStatus #

gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> EntityStatus -> m EntityStatus #

gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> EntityStatus -> m EntityStatus #

Ord EntityStatus Source # 
Read EntityStatus Source # 
Show EntityStatus Source # 
Generic EntityStatus Source # 

Associated Types

type Rep EntityStatus :: * -> * #

FromJSON EntityStatus Source # 
Hashable EntityStatus Source # 
NFData EntityStatus Source # 

Methods

rnf :: EntityStatus -> () #

ToHeader EntityStatus Source # 
ToQuery EntityStatus Source # 
ToByteString EntityStatus Source # 
FromText EntityStatus Source # 
ToText EntityStatus Source # 

Methods

toText :: EntityStatus -> Text #

type Rep EntityStatus Source # 
type Rep EntityStatus = D1 (MetaData "EntityStatus" "Network.AWS.MachineLearning.Types.Sum" "amazonka-ml-1.4.3-DbYWzKGDN8K1aNJlQiNb67" False) ((:+:) ((:+:) (C1 (MetaCons "ESCompleted" PrefixI False) U1) (C1 (MetaCons "ESDeleted" PrefixI False) U1)) ((:+:) (C1 (MetaCons "ESFailed" PrefixI False) U1) ((:+:) (C1 (MetaCons "ESInprogress" PrefixI False) U1) (C1 (MetaCons "ESPending" PrefixI False) U1))))

EvaluationFilterVariable

data EvaluationFilterVariable Source #

A 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.

Instances

Bounded EvaluationFilterVariable Source # 
Enum EvaluationFilterVariable Source # 
Eq EvaluationFilterVariable Source # 
Data EvaluationFilterVariable Source # 

Methods

gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> EvaluationFilterVariable -> c EvaluationFilterVariable #

gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c EvaluationFilterVariable #

toConstr :: EvaluationFilterVariable -> Constr #

dataTypeOf :: EvaluationFilterVariable -> DataType #

dataCast1 :: Typeable (* -> *) t => (forall d. Data d => c (t d)) -> Maybe (c EvaluationFilterVariable) #

dataCast2 :: Typeable (* -> * -> *) t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c EvaluationFilterVariable) #

gmapT :: (forall b. Data b => b -> b) -> EvaluationFilterVariable -> EvaluationFilterVariable #

gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> EvaluationFilterVariable -> r #

gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> EvaluationFilterVariable -> r #

gmapQ :: (forall d. Data d => d -> u) -> EvaluationFilterVariable -> [u] #

gmapQi :: Int -> (forall d. Data d => d -> u) -> EvaluationFilterVariable -> u #

gmapM :: Monad m => (forall d. Data d => d -> m d) -> EvaluationFilterVariable -> m EvaluationFilterVariable #

gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> EvaluationFilterVariable -> m EvaluationFilterVariable #

gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> EvaluationFilterVariable -> m EvaluationFilterVariable #

Ord EvaluationFilterVariable Source # 
Read EvaluationFilterVariable Source # 
Show EvaluationFilterVariable Source # 
Generic EvaluationFilterVariable Source # 
ToJSON EvaluationFilterVariable Source # 
Hashable EvaluationFilterVariable Source # 
NFData EvaluationFilterVariable Source # 
ToHeader EvaluationFilterVariable Source # 
ToQuery EvaluationFilterVariable Source # 
ToByteString EvaluationFilterVariable Source # 
FromText EvaluationFilterVariable Source # 
ToText EvaluationFilterVariable Source # 
type Rep EvaluationFilterVariable Source # 
type Rep EvaluationFilterVariable = D1 (MetaData "EvaluationFilterVariable" "Network.AWS.MachineLearning.Types.Sum" "amazonka-ml-1.4.3-DbYWzKGDN8K1aNJlQiNb67" False) ((:+:) ((:+:) ((:+:) (C1 (MetaCons "EvalCreatedAt" PrefixI False) U1) (C1 (MetaCons "EvalDataSourceId" PrefixI False) U1)) ((:+:) (C1 (MetaCons "EvalDataURI" PrefixI False) U1) (C1 (MetaCons "EvalIAMUser" PrefixI False) U1))) ((:+:) ((:+:) (C1 (MetaCons "EvalLastUpdatedAt" PrefixI False) U1) (C1 (MetaCons "EvalMLModelId" PrefixI False) U1)) ((:+:) (C1 (MetaCons "EvalName" PrefixI False) U1) (C1 (MetaCons "EvalStatus" PrefixI False) U1))))

MLModelFilterVariable

data MLModelFilterVariable Source #

Instances

Bounded MLModelFilterVariable Source # 
Enum MLModelFilterVariable Source # 
Eq MLModelFilterVariable Source # 
Data MLModelFilterVariable Source # 

Methods

gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> MLModelFilterVariable -> c MLModelFilterVariable #

gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c MLModelFilterVariable #

toConstr :: MLModelFilterVariable -> Constr #

dataTypeOf :: MLModelFilterVariable -> DataType #

dataCast1 :: Typeable (* -> *) t => (forall d. Data d => c (t d)) -> Maybe (c MLModelFilterVariable) #

dataCast2 :: Typeable (* -> * -> *) t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c MLModelFilterVariable) #

gmapT :: (forall b. Data b => b -> b) -> MLModelFilterVariable -> MLModelFilterVariable #

gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> MLModelFilterVariable -> r #

gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> MLModelFilterVariable -> r #

gmapQ :: (forall d. Data d => d -> u) -> MLModelFilterVariable -> [u] #

gmapQi :: Int -> (forall d. Data d => d -> u) -> MLModelFilterVariable -> u #

gmapM :: Monad m => (forall d. Data d => d -> m d) -> MLModelFilterVariable -> m MLModelFilterVariable #

gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> MLModelFilterVariable -> m MLModelFilterVariable #

gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> MLModelFilterVariable -> m MLModelFilterVariable #

Ord MLModelFilterVariable Source # 
Read MLModelFilterVariable Source # 
Show MLModelFilterVariable Source # 
Generic MLModelFilterVariable Source # 
ToJSON MLModelFilterVariable Source # 
Hashable MLModelFilterVariable Source # 
NFData MLModelFilterVariable Source # 

Methods

rnf :: MLModelFilterVariable -> () #

ToHeader MLModelFilterVariable Source # 
ToQuery MLModelFilterVariable Source # 
ToByteString MLModelFilterVariable Source # 
FromText MLModelFilterVariable Source # 
ToText MLModelFilterVariable Source # 
type Rep MLModelFilterVariable Source # 
type Rep MLModelFilterVariable = D1 (MetaData "MLModelFilterVariable" "Network.AWS.MachineLearning.Types.Sum" "amazonka-ml-1.4.3-DbYWzKGDN8K1aNJlQiNb67" False) ((:+:) ((:+:) ((:+:) (C1 (MetaCons "MLMFVAlgorithm" PrefixI False) U1) (C1 (MetaCons "MLMFVCreatedAt" PrefixI False) U1)) ((:+:) (C1 (MetaCons "MLMFVIAMUser" PrefixI False) U1) ((:+:) (C1 (MetaCons "MLMFVLastUpdatedAt" PrefixI False) U1) (C1 (MetaCons "MLMFVMLModelType" PrefixI False) U1)))) ((:+:) ((:+:) (C1 (MetaCons "MLMFVName" PrefixI False) U1) (C1 (MetaCons "MLMFVRealtimeEndpointStatus" PrefixI False) U1)) ((:+:) (C1 (MetaCons "MLMFVStatus" PrefixI False) U1) ((:+:) (C1 (MetaCons "MLMFVTrainingDataSourceId" PrefixI False) U1) (C1 (MetaCons "MLMFVTrainingDataURI" PrefixI False) U1)))))

MLModelType

data MLModelType Source #

Constructors

Binary 
Multiclass 
Regression 

Instances

Bounded MLModelType Source # 
Enum MLModelType Source # 
Eq MLModelType Source # 
Data MLModelType Source # 

Methods

gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> MLModelType -> c MLModelType #

gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c MLModelType #

toConstr :: MLModelType -> Constr #

dataTypeOf :: MLModelType -> DataType #

dataCast1 :: Typeable (* -> *) t => (forall d. Data d => c (t d)) -> Maybe (c MLModelType) #

dataCast2 :: Typeable (* -> * -> *) t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c MLModelType) #

gmapT :: (forall b. Data b => b -> b) -> MLModelType -> MLModelType #

gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> MLModelType -> r #

gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> MLModelType -> r #

gmapQ :: (forall d. Data d => d -> u) -> MLModelType -> [u] #

gmapQi :: Int -> (forall d. Data d => d -> u) -> MLModelType -> u #

gmapM :: Monad m => (forall d. Data d => d -> m d) -> MLModelType -> m MLModelType #

gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> MLModelType -> m MLModelType #

gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> MLModelType -> m MLModelType #

Ord MLModelType Source # 
Read MLModelType Source # 
Show MLModelType Source # 
Generic MLModelType Source # 

Associated Types

type Rep MLModelType :: * -> * #

ToJSON MLModelType Source # 
FromJSON MLModelType Source # 
Hashable MLModelType Source # 
NFData MLModelType Source # 

Methods

rnf :: MLModelType -> () #

ToHeader MLModelType Source # 
ToQuery MLModelType Source # 
ToByteString MLModelType Source # 
FromText MLModelType Source # 
ToText MLModelType Source # 

Methods

toText :: MLModelType -> Text #

type Rep MLModelType Source # 
type Rep MLModelType = D1 (MetaData "MLModelType" "Network.AWS.MachineLearning.Types.Sum" "amazonka-ml-1.4.3-DbYWzKGDN8K1aNJlQiNb67" False) ((:+:) (C1 (MetaCons "Binary" PrefixI False) U1) ((:+:) (C1 (MetaCons "Multiclass" PrefixI False) U1) (C1 (MetaCons "Regression" PrefixI False) U1)))

RealtimeEndpointStatus

data RealtimeEndpointStatus Source #

Constructors

Failed 
None 
Ready 
Updating 

Instances

Bounded RealtimeEndpointStatus Source # 
Enum RealtimeEndpointStatus Source # 
Eq RealtimeEndpointStatus Source # 
Data RealtimeEndpointStatus Source # 

Methods

gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> RealtimeEndpointStatus -> c RealtimeEndpointStatus #

gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c RealtimeEndpointStatus #

toConstr :: RealtimeEndpointStatus -> Constr #

dataTypeOf :: RealtimeEndpointStatus -> DataType #

dataCast1 :: Typeable (* -> *) t => (forall d. Data d => c (t d)) -> Maybe (c RealtimeEndpointStatus) #

dataCast2 :: Typeable (* -> * -> *) t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c RealtimeEndpointStatus) #

gmapT :: (forall b. Data b => b -> b) -> RealtimeEndpointStatus -> RealtimeEndpointStatus #

gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> RealtimeEndpointStatus -> r #

gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> RealtimeEndpointStatus -> r #

gmapQ :: (forall d. Data d => d -> u) -> RealtimeEndpointStatus -> [u] #

gmapQi :: Int -> (forall d. Data d => d -> u) -> RealtimeEndpointStatus -> u #

gmapM :: Monad m => (forall d. Data d => d -> m d) -> RealtimeEndpointStatus -> m RealtimeEndpointStatus #

gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> RealtimeEndpointStatus -> m RealtimeEndpointStatus #

gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> RealtimeEndpointStatus -> m RealtimeEndpointStatus #

Ord RealtimeEndpointStatus Source # 
Read RealtimeEndpointStatus Source # 
Show RealtimeEndpointStatus Source # 
Generic RealtimeEndpointStatus Source # 
FromJSON RealtimeEndpointStatus Source # 
Hashable RealtimeEndpointStatus Source # 
NFData RealtimeEndpointStatus Source # 

Methods

rnf :: RealtimeEndpointStatus -> () #

ToHeader RealtimeEndpointStatus Source # 
ToQuery RealtimeEndpointStatus Source # 
ToByteString RealtimeEndpointStatus Source # 
FromText RealtimeEndpointStatus Source # 
ToText RealtimeEndpointStatus Source # 
type Rep RealtimeEndpointStatus Source # 
type Rep RealtimeEndpointStatus = D1 (MetaData "RealtimeEndpointStatus" "Network.AWS.MachineLearning.Types.Sum" "amazonka-ml-1.4.3-DbYWzKGDN8K1aNJlQiNb67" False) ((:+:) ((:+:) (C1 (MetaCons "Failed" PrefixI False) U1) (C1 (MetaCons "None" PrefixI False) U1)) ((:+:) (C1 (MetaCons "Ready" PrefixI False) U1) (C1 (MetaCons "Updating" PrefixI False) U1)))

SortOrder

data SortOrder Source #

The sort order specified in a listing condition. Possible values include the following:

  • asc - Present the information in ascending order (from A-Z).
  • dsc - Present the information in descending order (from Z-A).

Constructors

Asc 
Dsc 

Instances

Bounded SortOrder Source # 
Enum SortOrder Source # 
Eq SortOrder Source # 
Data SortOrder Source # 

Methods

gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> SortOrder -> c SortOrder #

gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c SortOrder #

toConstr :: SortOrder -> Constr #

dataTypeOf :: SortOrder -> DataType #

dataCast1 :: Typeable (* -> *) t => (forall d. Data d => c (t d)) -> Maybe (c SortOrder) #

dataCast2 :: Typeable (* -> * -> *) t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c SortOrder) #

gmapT :: (forall b. Data b => b -> b) -> SortOrder -> SortOrder #

gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> SortOrder -> r #

gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> SortOrder -> r #

gmapQ :: (forall d. Data d => d -> u) -> SortOrder -> [u] #

gmapQi :: Int -> (forall d. Data d => d -> u) -> SortOrder -> u #

gmapM :: Monad m => (forall d. Data d => d -> m d) -> SortOrder -> m SortOrder #

gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> SortOrder -> m SortOrder #

gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> SortOrder -> m SortOrder #

Ord SortOrder Source # 
Read SortOrder Source # 
Show SortOrder Source # 
Generic SortOrder Source # 

Associated Types

type Rep SortOrder :: * -> * #

ToJSON SortOrder Source # 
Hashable SortOrder Source # 
NFData SortOrder Source # 

Methods

rnf :: SortOrder -> () #

ToHeader SortOrder Source # 
ToQuery SortOrder Source # 
ToByteString SortOrder Source # 

Methods

toBS :: SortOrder -> ByteString #

FromText SortOrder Source # 
ToText SortOrder Source # 

Methods

toText :: SortOrder -> Text #

type Rep SortOrder Source # 
type Rep SortOrder = D1 (MetaData "SortOrder" "Network.AWS.MachineLearning.Types.Sum" "amazonka-ml-1.4.3-DbYWzKGDN8K1aNJlQiNb67" False) ((:+:) (C1 (MetaCons "Asc" PrefixI False) U1) (C1 (MetaCons "Dsc" PrefixI False) U1))

BatchPrediction

data BatchPrediction Source #

Represents the output of GetBatchPrediction operation.

The content consists of the detailed metadata, the status, and the data file information of a Batch Prediction.

See: batchPrediction smart constructor.

Instances

Eq BatchPrediction Source # 
Data BatchPrediction Source # 

Methods

gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> BatchPrediction -> c BatchPrediction #

gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c BatchPrediction #

toConstr :: BatchPrediction -> Constr #

dataTypeOf :: BatchPrediction -> DataType #

dataCast1 :: Typeable (* -> *) t => (forall d. Data d => c (t d)) -> Maybe (c BatchPrediction) #

dataCast2 :: Typeable (* -> * -> *) t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c BatchPrediction) #

gmapT :: (forall b. Data b => b -> b) -> BatchPrediction -> BatchPrediction #

gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> BatchPrediction -> r #

gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> BatchPrediction -> r #

gmapQ :: (forall d. Data d => d -> u) -> BatchPrediction -> [u] #

gmapQi :: Int -> (forall d. Data d => d -> u) -> BatchPrediction -> u #

gmapM :: Monad m => (forall d. Data d => d -> m d) -> BatchPrediction -> m BatchPrediction #

gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> BatchPrediction -> m BatchPrediction #

gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> BatchPrediction -> m BatchPrediction #

Read BatchPrediction Source # 
Show BatchPrediction Source # 
Generic BatchPrediction Source # 
FromJSON BatchPrediction Source # 
Hashable BatchPrediction Source # 
NFData BatchPrediction Source # 

Methods

rnf :: BatchPrediction -> () #

type Rep BatchPrediction Source # 
type Rep BatchPrediction = D1 (MetaData "BatchPrediction" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.4.3-DbYWzKGDN8K1aNJlQiNb67" False) (C1 (MetaCons "BatchPrediction'" PrefixI True) ((:*:) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_bpStatus") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe EntityStatus))) (S1 (MetaSel (Just Symbol "_bpLastUpdatedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX)))) ((:*:) (S1 (MetaSel (Just Symbol "_bpCreatedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX))) ((:*:) (S1 (MetaSel (Just Symbol "_bpInputDataLocationS3") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) (S1 (MetaSel (Just Symbol "_bpMLModelId") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)))))) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_bpBatchPredictionDataSourceId") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) ((:*:) (S1 (MetaSel (Just Symbol "_bpBatchPredictionId") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) (S1 (MetaSel (Just Symbol "_bpCreatedByIAMUser") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))))) ((:*:) (S1 (MetaSel (Just Symbol "_bpName") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) ((:*:) (S1 (MetaSel (Just Symbol "_bpMessage") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) (S1 (MetaSel (Just Symbol "_bpOutputURI") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))))))))

batchPrediction :: BatchPrediction Source #

Creates a value of BatchPrediction with the minimum fields required to make a request.

Use one of the following lenses to modify other fields as desired:

bpStatus :: Lens' BatchPrediction (Maybe EntityStatus) Source #

The status of the BatchPrediction. 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 peform a batch prediction did not run to completion. It is not usable.
  • COMPLETED - The batch prediction process completed successfully.
  • DELETED - The BatchPrediction is marked as deleted. It is not usable.

bpLastUpdatedAt :: Lens' BatchPrediction (Maybe UTCTime) Source #

The time of the most recent edit to the BatchPrediction. The time is expressed in epoch time.

bpCreatedAt :: Lens' BatchPrediction (Maybe UTCTime) Source #

The time that the BatchPrediction was created. The time is expressed in epoch time.

bpInputDataLocationS3 :: Lens' BatchPrediction (Maybe Text) Source #

The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

bpMLModelId :: Lens' BatchPrediction (Maybe Text) Source #

The ID of the MLModel that generated predictions for the BatchPrediction request.

bpBatchPredictionDataSourceId :: Lens' BatchPrediction (Maybe Text) Source #

The ID of the DataSource that points to the group of observations to predict.

bpBatchPredictionId :: Lens' BatchPrediction (Maybe Text) Source #

The ID assigned to the BatchPrediction at creation. This value should be identical to the value of the BatchPredictionID in the request.

bpCreatedByIAMUser :: Lens' BatchPrediction (Maybe Text) Source #

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.

bpName :: Lens' BatchPrediction (Maybe Text) Source #

A user-supplied name or description of the BatchPrediction.

bpMessage :: Lens' BatchPrediction (Maybe Text) Source #

A description of the most recent details about processing the batch prediction request.

bpOutputURI :: Lens' BatchPrediction (Maybe Text) Source #

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: ':', '//', '/./', '/../'.

DataSource

data DataSource Source #

Represents 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: dataSource smart constructor.

Instances

Eq DataSource Source # 
Data DataSource Source # 

Methods

gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> DataSource -> c DataSource #

gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c DataSource #

toConstr :: DataSource -> Constr #

dataTypeOf :: DataSource -> DataType #

dataCast1 :: Typeable (* -> *) t => (forall d. Data d => c (t d)) -> Maybe (c DataSource) #

dataCast2 :: Typeable (* -> * -> *) t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c DataSource) #

gmapT :: (forall b. Data b => b -> b) -> DataSource -> DataSource #

gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> DataSource -> r #

gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> DataSource -> r #

gmapQ :: (forall d. Data d => d -> u) -> DataSource -> [u] #

gmapQi :: Int -> (forall d. Data d => d -> u) -> DataSource -> u #

gmapM :: Monad m => (forall d. Data d => d -> m d) -> DataSource -> m DataSource #

gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> DataSource -> m DataSource #

gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> DataSource -> m DataSource #

Read DataSource Source # 
Show DataSource Source # 
Generic DataSource Source # 

Associated Types

type Rep DataSource :: * -> * #

FromJSON DataSource Source # 
Hashable DataSource Source # 
NFData DataSource Source # 

Methods

rnf :: DataSource -> () #

type Rep DataSource Source # 
type Rep DataSource = D1 (MetaData "DataSource" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.4.3-DbYWzKGDN8K1aNJlQiNb67" False) (C1 (MetaCons "DataSource'" PrefixI True) ((:*:) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_dsStatus") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe EntityStatus))) ((:*:) (S1 (MetaSel (Just Symbol "_dsNumberOfFiles") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Integer))) (S1 (MetaSel (Just Symbol "_dsLastUpdatedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX))))) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_dsCreatedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX))) (S1 (MetaSel (Just Symbol "_dsDataSourceId") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)))) ((:*:) (S1 (MetaSel (Just Symbol "_dsRDSMetadata") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe RDSMetadata))) (S1 (MetaSel (Just Symbol "_dsDataSizeInBytes") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Integer)))))) ((:*:) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_dsCreatedByIAMUser") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) (S1 (MetaSel (Just Symbol "_dsName") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)))) ((:*:) (S1 (MetaSel (Just Symbol "_dsDataLocationS3") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) (S1 (MetaSel (Just Symbol "_dsComputeStatistics") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Bool))))) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_dsMessage") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) (S1 (MetaSel (Just Symbol "_dsRedshiftMetadata") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe RedshiftMetadata)))) ((:*:) (S1 (MetaSel (Just Symbol "_dsDataRearrangement") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) (S1 (MetaSel (Just Symbol "_dsRoleARN") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))))))))

dsStatus :: Lens' DataSource (Maybe EntityStatus) Source #

The current status of the DataSource. 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 DataSource 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.

dsNumberOfFiles :: Lens' DataSource (Maybe Integer) Source #

The number of data files referenced by the DataSource.

dsLastUpdatedAt :: Lens' DataSource (Maybe UTCTime) Source #

The time of the most recent edit to the BatchPrediction. The time is expressed in epoch time.

dsCreatedAt :: Lens' DataSource (Maybe UTCTime) Source #

The time that the DataSource was created. The time is expressed in epoch time.

dsDataSourceId :: Lens' DataSource (Maybe Text) Source #

The ID that is assigned to the DataSource during creation.

dsDataSizeInBytes :: Lens' DataSource (Maybe Integer) Source #

The total number of observations contained in the data files that the DataSource references.

dsCreatedByIAMUser :: Lens' DataSource (Maybe Text) Source #

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.

dsName :: Lens' DataSource (Maybe Text) Source #

A user-supplied name or description of the DataSource.

dsDataLocationS3 :: Lens' DataSource (Maybe Text) Source #

The location and name of the data in Amazon Simple Storage Service (Amazon S3) that is used by a DataSource.

dsComputeStatistics :: Lens' DataSource (Maybe Bool) Source #

The parameter is true if statistics need to be generated from the observation data.

dsMessage :: Lens' DataSource (Maybe Text) Source #

A description of the most recent details about creating the DataSource.

dsDataRearrangement :: Lens' DataSource (Maybe Text) Source #

A JSON string that represents the splitting requirement of a Datasource.

dsRoleARN :: Lens' DataSource (Maybe Text) Source #

Undocumented member.

Evaluation

data Evaluation Source #

Represents the output of GetEvaluation operation.

The content consists of the detailed metadata and data file information and the current status of the Evaluation.

See: evaluation smart constructor.

Instances

Eq Evaluation Source # 
Data Evaluation Source # 

Methods

gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> Evaluation -> c Evaluation #

gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c Evaluation #

toConstr :: Evaluation -> Constr #

dataTypeOf :: Evaluation -> DataType #

dataCast1 :: Typeable (* -> *) t => (forall d. Data d => c (t d)) -> Maybe (c Evaluation) #

dataCast2 :: Typeable (* -> * -> *) t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c Evaluation) #

gmapT :: (forall b. Data b => b -> b) -> Evaluation -> Evaluation #

gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> Evaluation -> r #

gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> Evaluation -> r #

gmapQ :: (forall d. Data d => d -> u) -> Evaluation -> [u] #

gmapQi :: Int -> (forall d. Data d => d -> u) -> Evaluation -> u #

gmapM :: Monad m => (forall d. Data d => d -> m d) -> Evaluation -> m Evaluation #

gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> Evaluation -> m Evaluation #

gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> Evaluation -> m Evaluation #

Read Evaluation Source # 
Show Evaluation Source # 
Generic Evaluation Source # 

Associated Types

type Rep Evaluation :: * -> * #

FromJSON Evaluation Source # 
Hashable Evaluation Source # 
NFData Evaluation Source # 

Methods

rnf :: Evaluation -> () #

type Rep Evaluation Source # 
type Rep Evaluation = D1 (MetaData "Evaluation" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.4.3-DbYWzKGDN8K1aNJlQiNb67" False) (C1 (MetaCons "Evaluation'" PrefixI True) ((:*:) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_eStatus") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe EntityStatus))) (S1 (MetaSel (Just Symbol "_ePerformanceMetrics") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe PerformanceMetrics)))) ((:*:) (S1 (MetaSel (Just Symbol "_eLastUpdatedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX))) ((:*:) (S1 (MetaSel (Just Symbol "_eCreatedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX))) (S1 (MetaSel (Just Symbol "_eInputDataLocationS3") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)))))) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_eMLModelId") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) ((:*:) (S1 (MetaSel (Just Symbol "_eCreatedByIAMUser") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) (S1 (MetaSel (Just Symbol "_eName") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))))) ((:*:) (S1 (MetaSel (Just Symbol "_eEvaluationId") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) ((:*:) (S1 (MetaSel (Just Symbol "_eMessage") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) (S1 (MetaSel (Just Symbol "_eEvaluationDataSourceId") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))))))))

evaluation :: Evaluation Source #

Creates a value of Evaluation with the minimum fields required to make a request.

Use one of the following lenses to modify other fields as desired:

eStatus :: Lens' Evaluation (Maybe EntityStatus) Source #

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 MLModel did not run to completion. It is not usable.
  • COMPLETED - The evaluation process completed successfully.
  • DELETED - The Evaluation is marked as deleted. It is not usable.

ePerformanceMetrics :: Lens' Evaluation (Maybe PerformanceMetrics) Source #

Measurements of how well the MLModel 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 MLModel uses the F1 score technique to measure performance.

For more information about performance metrics, please see the Amazon Machine Learning Developer Guide.

eLastUpdatedAt :: Lens' Evaluation (Maybe UTCTime) Source #

The time of the most recent edit to the Evaluation. The time is expressed in epoch time.

eCreatedAt :: Lens' Evaluation (Maybe UTCTime) Source #

The time that the Evaluation was created. The time is expressed in epoch time.

eInputDataLocationS3 :: Lens' Evaluation (Maybe Text) Source #

The location and name of the data in Amazon Simple Storage Server (Amazon S3) that is used in the evaluation.

eMLModelId :: Lens' Evaluation (Maybe Text) Source #

The ID of the MLModel that is the focus of the evaluation.

eCreatedByIAMUser :: Lens' Evaluation (Maybe Text) Source #

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.

eName :: Lens' Evaluation (Maybe Text) Source #

A user-supplied name or description of the Evaluation.

eEvaluationId :: Lens' Evaluation (Maybe Text) Source #

The ID that is assigned to the Evaluation at creation.

eMessage :: Lens' Evaluation (Maybe Text) Source #

A description of the most recent details about evaluating the MLModel.

eEvaluationDataSourceId :: Lens' Evaluation (Maybe Text) Source #

The ID of the DataSource that is used to evaluate the MLModel.

MLModel

data MLModel Source #

Represents the output of a GetMLModel operation.

The content consists of the detailed metadata and the current status of the MLModel.

See: mLModel smart constructor.

Instances

Eq MLModel Source # 

Methods

(==) :: MLModel -> MLModel -> Bool #

(/=) :: MLModel -> MLModel -> Bool #

Data MLModel Source # 

Methods

gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> MLModel -> c MLModel #

gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c MLModel #

toConstr :: MLModel -> Constr #

dataTypeOf :: MLModel -> DataType #

dataCast1 :: Typeable (* -> *) t => (forall d. Data d => c (t d)) -> Maybe (c MLModel) #

dataCast2 :: Typeable (* -> * -> *) t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c MLModel) #

gmapT :: (forall b. Data b => b -> b) -> MLModel -> MLModel #

gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> MLModel -> r #

gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> MLModel -> r #

gmapQ :: (forall d. Data d => d -> u) -> MLModel -> [u] #

gmapQi :: Int -> (forall d. Data d => d -> u) -> MLModel -> u #

gmapM :: Monad m => (forall d. Data d => d -> m d) -> MLModel -> m MLModel #

gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> MLModel -> m MLModel #

gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> MLModel -> m MLModel #

Read MLModel Source # 
Show MLModel Source # 
Generic MLModel Source # 

Associated Types

type Rep MLModel :: * -> * #

Methods

from :: MLModel -> Rep MLModel x #

to :: Rep MLModel x -> MLModel #

FromJSON MLModel Source # 
Hashable MLModel Source # 

Methods

hashWithSalt :: Int -> MLModel -> Int #

hash :: MLModel -> Int #

NFData MLModel Source # 

Methods

rnf :: MLModel -> () #

type Rep MLModel Source # 
type Rep MLModel = D1 (MetaData "MLModel" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.4.3-DbYWzKGDN8K1aNJlQiNb67" False) (C1 (MetaCons "MLModel'" PrefixI True) ((:*:) ((:*:) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_mlmStatus") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe EntityStatus))) (S1 (MetaSel (Just Symbol "_mlmLastUpdatedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX)))) ((:*:) (S1 (MetaSel (Just Symbol "_mlmTrainingParameters") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe (Map Text Text)))) (S1 (MetaSel (Just Symbol "_mlmScoreThresholdLastUpdatedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX))))) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_mlmCreatedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX))) (S1 (MetaSel (Just Symbol "_mlmInputDataLocationS3") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)))) ((:*:) (S1 (MetaSel (Just Symbol "_mlmMLModelId") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) (S1 (MetaSel (Just Symbol "_mlmSizeInBytes") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Integer)))))) ((:*:) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_mlmScoreThreshold") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Double))) (S1 (MetaSel (Just Symbol "_mlmAlgorithm") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Algorithm)))) ((:*:) (S1 (MetaSel (Just Symbol "_mlmCreatedByIAMUser") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) (S1 (MetaSel (Just Symbol "_mlmName") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))))) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_mlmEndpointInfo") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe RealtimeEndpointInfo))) (S1 (MetaSel (Just Symbol "_mlmTrainingDataSourceId") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)))) ((:*:) (S1 (MetaSel (Just Symbol "_mlmMessage") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) (S1 (MetaSel (Just Symbol "_mlmMLModelType") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe MLModelType))))))))

mlmStatus :: Lens' MLModel (Maybe EntityStatus) Source #

The current status of an MLModel. 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 MLModel did not run to completion. It is not usable.
  • COMPLETED - The creation process completed successfully.
  • DELETED - The MLModel is marked as deleted. It is not usable.

mlmLastUpdatedAt :: Lens' MLModel (Maybe UTCTime) Source #

The time of the most recent edit to the MLModel. The time is expressed in epoch time.

mlmTrainingParameters :: Lens' MLModel (HashMap Text Text) Source #

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.l1RegularizationAmount' - 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, specify a small value, such as 1.0E-04 or 1.0E-08.

    The value is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L1 normalization. The parameter cannot be used when L2 is specified. Use this parameter sparingly.

  • 'sgd.l2RegularizationAmount' - 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, specify a small value, such as 1.0E-04 or 1.0E-08.

    The valus is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This cannot be used when L1 is specified. Use this parameter sparingly.

  • 'sgd.maxPasses' - 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.maxMLModelSizeInBytes' - Maximum allowed size of the model. Depending on the input data, the model size might affect performance.

    The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.

mlmScoreThresholdLastUpdatedAt :: Lens' MLModel (Maybe UTCTime) Source #

The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time.

mlmCreatedAt :: Lens' MLModel (Maybe UTCTime) Source #

The time that the MLModel was created. The time is expressed in epoch time.

mlmInputDataLocationS3 :: Lens' MLModel (Maybe Text) Source #

The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

mlmMLModelId :: Lens' MLModel (Maybe Text) Source #

The ID assigned to the MLModel at creation.

mlmSizeInBytes :: Lens' MLModel (Maybe Integer) Source #

Undocumented member.

mlmAlgorithm :: Lens' MLModel (Maybe Algorithm) Source #

The algorithm used to train the MLModel. The following algorithm is supported:

  • SGD -- Stochastic gradient descent. The goal of SGD is to minimize the gradient of the loss function.

mlmCreatedByIAMUser :: Lens' MLModel (Maybe Text) Source #

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.

mlmName :: Lens' MLModel (Maybe Text) Source #

A user-supplied name or description of the MLModel.

mlmTrainingDataSourceId :: Lens' MLModel (Maybe Text) Source #

The ID of the training DataSource. The CreateMLModel operation uses the TrainingDataSourceId.

mlmMessage :: Lens' MLModel (Maybe Text) Source #

A description of the most recent details about accessing the MLModel.

mlmMLModelType :: Lens' MLModel (Maybe MLModelType) Source #

Identifies the MLModel category. The following are the available types:

  • REGRESSION - Produces a numeric result. For example, "What listing price should a house have?".
  • BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?".
  • MULTICLASS - Produces more than two possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?".

PerformanceMetrics

data PerformanceMetrics Source #

Measurements 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 MLModel uses the F1 score technique to measure performance.

For more information about performance metrics, please see the Amazon Machine Learning Developer Guide.

See: performanceMetrics smart constructor.

Instances

Eq PerformanceMetrics Source # 
Data PerformanceMetrics Source # 

Methods

gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> PerformanceMetrics -> c PerformanceMetrics #

gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c PerformanceMetrics #

toConstr :: PerformanceMetrics -> Constr #

dataTypeOf :: PerformanceMetrics -> DataType #

dataCast1 :: Typeable (* -> *) t => (forall d. Data d => c (t d)) -> Maybe (c PerformanceMetrics) #

dataCast2 :: Typeable (* -> * -> *) t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c PerformanceMetrics) #

gmapT :: (forall b. Data b => b -> b) -> PerformanceMetrics -> PerformanceMetrics #

gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> PerformanceMetrics -> r #

gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> PerformanceMetrics -> r #

gmapQ :: (forall d. Data d => d -> u) -> PerformanceMetrics -> [u] #

gmapQi :: Int -> (forall d. Data d => d -> u) -> PerformanceMetrics -> u #

gmapM :: Monad m => (forall d. Data d => d -> m d) -> PerformanceMetrics -> m PerformanceMetrics #

gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> PerformanceMetrics -> m PerformanceMetrics #

gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> PerformanceMetrics -> m PerformanceMetrics #

Read PerformanceMetrics Source # 
Show PerformanceMetrics Source # 
Generic PerformanceMetrics Source # 
FromJSON PerformanceMetrics Source # 
Hashable PerformanceMetrics Source # 
NFData PerformanceMetrics Source # 

Methods

rnf :: PerformanceMetrics -> () #

type Rep PerformanceMetrics Source # 
type Rep PerformanceMetrics = D1 (MetaData "PerformanceMetrics" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.4.3-DbYWzKGDN8K1aNJlQiNb67" True) (C1 (MetaCons "PerformanceMetrics'" PrefixI True) (S1 (MetaSel (Just Symbol "_pmProperties") NoSourceUnpackedness NoSourceStrictness DecidedLazy) (Rec0 (Maybe (Map Text Text)))))

performanceMetrics :: PerformanceMetrics Source #

Creates a value of PerformanceMetrics with the minimum fields required to make a request.

Use one of the following lenses to modify other fields as desired:

Prediction

data Prediction Source #

The output from a Predict operation:

  • Details - Contains the following attributes: DetailsAttributes.PREDICTIVE_MODEL_TYPE - REGRESSION | BINARY | MULTICLASS DetailsAttributes.ALGORITHM - SGD
  • PredictedLabel - 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: prediction smart constructor.

Instances

Eq Prediction Source # 
Data Prediction Source # 

Methods

gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> Prediction -> c Prediction #

gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c Prediction #

toConstr :: Prediction -> Constr #

dataTypeOf :: Prediction -> DataType #

dataCast1 :: Typeable (* -> *) t => (forall d. Data d => c (t d)) -> Maybe (c Prediction) #

dataCast2 :: Typeable (* -> * -> *) t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c Prediction) #

gmapT :: (forall b. Data b => b -> b) -> Prediction -> Prediction #

gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> Prediction -> r #

gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> Prediction -> r #

gmapQ :: (forall d. Data d => d -> u) -> Prediction -> [u] #

gmapQi :: Int -> (forall d. Data d => d -> u) -> Prediction -> u #

gmapM :: Monad m => (forall d. Data d => d -> m d) -> Prediction -> m Prediction #

gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> Prediction -> m Prediction #

gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> Prediction -> m Prediction #

Read Prediction Source # 
Show Prediction Source # 
Generic Prediction Source # 

Associated Types

type Rep Prediction :: * -> * #

FromJSON Prediction Source # 
Hashable Prediction Source # 
NFData Prediction Source # 

Methods

rnf :: Prediction -> () #

type Rep Prediction Source # 
type Rep Prediction = D1 (MetaData "Prediction" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.4.3-DbYWzKGDN8K1aNJlQiNb67" False) (C1 (MetaCons "Prediction'" PrefixI True) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_pPredictedValue") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Double))) (S1 (MetaSel (Just Symbol "_pPredictedLabel") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)))) ((:*:) (S1 (MetaSel (Just Symbol "_pPredictedScores") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe (Map Text Double)))) (S1 (MetaSel (Just Symbol "_pDetails") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe (Map DetailsAttributes Text)))))))

prediction :: Prediction Source #

Creates a value of Prediction with the minimum fields required to make a request.

Use one of the following lenses to modify other fields as desired:

pPredictedValue :: Lens' Prediction (Maybe Double) Source #

The prediction value for REGRESSION MLModel.

pPredictedLabel :: Lens' Prediction (Maybe Text) Source #

The prediction label for either a BINARY or MULTICLASS MLModel.

RDSDataSpec

data RDSDataSpec Source #

The data specification of an Amazon Relational Database Service (Amazon RDS) DataSource.

See: rdsDataSpec smart constructor.

Instances

Eq RDSDataSpec Source # 
Data RDSDataSpec Source # 

Methods

gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> RDSDataSpec -> c RDSDataSpec #

gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c RDSDataSpec #

toConstr :: RDSDataSpec -> Constr #

dataTypeOf :: RDSDataSpec -> DataType #

dataCast1 :: Typeable (* -> *) t => (forall d. Data d => c (t d)) -> Maybe (c RDSDataSpec) #

dataCast2 :: Typeable (* -> * -> *) t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c RDSDataSpec) #

gmapT :: (forall b. Data b => b -> b) -> RDSDataSpec -> RDSDataSpec #

gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> RDSDataSpec -> r #

gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> RDSDataSpec -> r #

gmapQ :: (forall d. Data d => d -> u) -> RDSDataSpec -> [u] #

gmapQi :: Int -> (forall d. Data d => d -> u) -> RDSDataSpec -> u #

gmapM :: Monad m => (forall d. Data d => d -> m d) -> RDSDataSpec -> m RDSDataSpec #

gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> RDSDataSpec -> m RDSDataSpec #

gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> RDSDataSpec -> m RDSDataSpec #

Read RDSDataSpec Source # 
Show RDSDataSpec Source # 
Generic RDSDataSpec Source # 

Associated Types

type Rep RDSDataSpec :: * -> * #

ToJSON RDSDataSpec Source # 
Hashable RDSDataSpec Source # 
NFData RDSDataSpec Source # 

Methods

rnf :: RDSDataSpec -> () #

type Rep RDSDataSpec Source # 
type Rep RDSDataSpec = D1 (MetaData "RDSDataSpec" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.4.3-DbYWzKGDN8K1aNJlQiNb67" False) (C1 (MetaCons "RDSDataSpec'" PrefixI True) ((:*:) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_rdsdsDataSchemaURI") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) (S1 (MetaSel (Just Symbol "_rdsdsDataSchema") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)))) ((:*:) (S1 (MetaSel (Just Symbol "_rdsdsDataRearrangement") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) ((:*:) (S1 (MetaSel (Just Symbol "_rdsdsDatabaseInformation") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 RDSDatabase)) (S1 (MetaSel (Just Symbol "_rdsdsSelectSqlQuery") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 Text))))) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_rdsdsDatabaseCredentials") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 RDSDatabaseCredentials)) ((:*:) (S1 (MetaSel (Just Symbol "_rdsdsS3StagingLocation") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 Text)) (S1 (MetaSel (Just Symbol "_rdsdsResourceRole") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 Text)))) ((:*:) (S1 (MetaSel (Just Symbol "_rdsdsServiceRole") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 Text)) ((:*:) (S1 (MetaSel (Just Symbol "_rdsdsSubnetId") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 Text)) (S1 (MetaSel (Just Symbol "_rdsdsSecurityGroupIds") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 [Text])))))))

rdsdsDataSchemaURI :: Lens' RDSDataSpec (Maybe Text) Source #

The Amazon S3 location of the DataSchema.

rdsdsDataSchema :: Lens' RDSDataSpec (Maybe Text) Source #

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" ] }

rdsdsDataRearrangement :: Lens' RDSDataSpec (Maybe Text) Source #

DataRearrangement - A JSON string that represents the splitting requirement of a DataSource.

Sample - ' "{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"'

rdsdsDatabaseInformation :: Lens' RDSDataSpec RDSDatabase Source #

Describes the DatabaseName and InstanceIdentifier of an an Amazon RDS database.

rdsdsSelectSqlQuery :: Lens' RDSDataSpec Text Source #

The query that is used to retrieve the observation data for the DataSource.

rdsdsDatabaseCredentials :: Lens' RDSDataSpec RDSDatabaseCredentials Source #

The AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon RDS database.

rdsdsS3StagingLocation :: Lens' RDSDataSpec Text Source #

The Amazon S3 location for staging Amazon RDS data. The data retrieved from Amazon RDS using SelectSqlQuery is stored in this location.

rdsdsResourceRole :: Lens' RDSDataSpec Text Source #

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 Role templates for data pipelines.

rdsdsServiceRole :: Lens' RDSDataSpec Text Source #

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 Role templates for data pipelines.

rdsdsSubnetId :: Lens' RDSDataSpec Text Source #

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.

rdsdsSecurityGroupIds :: Lens' RDSDataSpec [Text] Source #

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.

RDSDatabase

data RDSDatabase Source #

The database details of an Amazon RDS database.

See: rdsDatabase smart constructor.

Instances

Eq RDSDatabase Source # 
Data RDSDatabase Source # 

Methods

gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> RDSDatabase -> c RDSDatabase #

gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c RDSDatabase #

toConstr :: RDSDatabase -> Constr #

dataTypeOf :: RDSDatabase -> DataType #

dataCast1 :: Typeable (* -> *) t => (forall d. Data d => c (t d)) -> Maybe (c RDSDatabase) #

dataCast2 :: Typeable (* -> * -> *) t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c RDSDatabase) #

gmapT :: (forall b. Data b => b -> b) -> RDSDatabase -> RDSDatabase #

gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> RDSDatabase -> r #

gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> RDSDatabase -> r #

gmapQ :: (forall d. Data d => d -> u) -> RDSDatabase -> [u] #

gmapQi :: Int -> (forall d. Data d => d -> u) -> RDSDatabase -> u #

gmapM :: Monad m => (forall d. Data d => d -> m d) -> RDSDatabase -> m RDSDatabase #

gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> RDSDatabase -> m RDSDatabase #

gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> RDSDatabase -> m RDSDatabase #

Read RDSDatabase Source # 
Show RDSDatabase Source # 
Generic RDSDatabase Source # 

Associated Types

type Rep RDSDatabase :: * -> * #

ToJSON RDSDatabase Source # 
FromJSON RDSDatabase Source # 
Hashable RDSDatabase Source # 
NFData RDSDatabase Source # 

Methods

rnf :: RDSDatabase -> () #

type Rep RDSDatabase Source # 
type Rep RDSDatabase = D1 (MetaData "RDSDatabase" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.4.3-DbYWzKGDN8K1aNJlQiNb67" False) (C1 (MetaCons "RDSDatabase'" PrefixI True) ((:*:) (S1 (MetaSel (Just Symbol "_rdsdInstanceIdentifier") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 Text)) (S1 (MetaSel (Just Symbol "_rdsdDatabaseName") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 Text))))

rdsDatabase Source #

Creates a value of RDSDatabase with the minimum fields required to make a request.

Use one of the following lenses to modify other fields as desired:

rdsdInstanceIdentifier :: Lens' RDSDatabase Text Source #

The ID of an RDS DB instance.

RDSDatabaseCredentials

data RDSDatabaseCredentials Source #

The database credentials to connect to a database on an RDS DB instance.

See: rdsDatabaseCredentials smart constructor.

Instances

Eq RDSDatabaseCredentials Source # 
Data RDSDatabaseCredentials Source # 

Methods

gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> RDSDatabaseCredentials -> c RDSDatabaseCredentials #

gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c RDSDatabaseCredentials #

toConstr :: RDSDatabaseCredentials -> Constr #

dataTypeOf :: RDSDatabaseCredentials -> DataType #

dataCast1 :: Typeable (* -> *) t => (forall d. Data d => c (t d)) -> Maybe (c RDSDatabaseCredentials) #

dataCast2 :: Typeable (* -> * -> *) t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c RDSDatabaseCredentials) #

gmapT :: (forall b. Data b => b -> b) -> RDSDatabaseCredentials -> RDSDatabaseCredentials #

gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> RDSDatabaseCredentials -> r #

gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> RDSDatabaseCredentials -> r #

gmapQ :: (forall d. Data d => d -> u) -> RDSDatabaseCredentials -> [u] #

gmapQi :: Int -> (forall d. Data d => d -> u) -> RDSDatabaseCredentials -> u #

gmapM :: Monad m => (forall d. Data d => d -> m d) -> RDSDatabaseCredentials -> m RDSDatabaseCredentials #

gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> RDSDatabaseCredentials -> m RDSDatabaseCredentials #

gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> RDSDatabaseCredentials -> m RDSDatabaseCredentials #

Read RDSDatabaseCredentials Source # 
Show RDSDatabaseCredentials Source # 
Generic RDSDatabaseCredentials Source # 
ToJSON RDSDatabaseCredentials Source # 
Hashable RDSDatabaseCredentials Source # 
NFData RDSDatabaseCredentials Source # 

Methods

rnf :: RDSDatabaseCredentials -> () #

type Rep RDSDatabaseCredentials Source # 
type Rep RDSDatabaseCredentials = D1 (MetaData "RDSDatabaseCredentials" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.4.3-DbYWzKGDN8K1aNJlQiNb67" False) (C1 (MetaCons "RDSDatabaseCredentials'" PrefixI True) ((:*:) (S1 (MetaSel (Just Symbol "_rdsdcUsername") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 Text)) (S1 (MetaSel (Just Symbol "_rdsdcPassword") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 Text))))

rdsDatabaseCredentials Source #

Creates a value of RDSDatabaseCredentials with the minimum fields required to make a request.

Use one of the following lenses to modify other fields as desired:

RDSMetadata

data RDSMetadata Source #

The datasource details that are specific to Amazon RDS.

See: rdsMetadata smart constructor.

Instances

Eq RDSMetadata Source # 
Data RDSMetadata Source # 

Methods

gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> RDSMetadata -> c RDSMetadata #

gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c RDSMetadata #

toConstr :: RDSMetadata -> Constr #

dataTypeOf :: RDSMetadata -> DataType #

dataCast1 :: Typeable (* -> *) t => (forall d. Data d => c (t d)) -> Maybe (c RDSMetadata) #

dataCast2 :: Typeable (* -> * -> *) t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c RDSMetadata) #

gmapT :: (forall b. Data b => b -> b) -> RDSMetadata -> RDSMetadata #

gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> RDSMetadata -> r #

gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> RDSMetadata -> r #

gmapQ :: (forall d. Data d => d -> u) -> RDSMetadata -> [u] #

gmapQi :: Int -> (forall d. Data d => d -> u) -> RDSMetadata -> u #

gmapM :: Monad m => (forall d. Data d => d -> m d) -> RDSMetadata -> m RDSMetadata #

gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> RDSMetadata -> m RDSMetadata #

gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> RDSMetadata -> m RDSMetadata #

Read RDSMetadata Source # 
Show RDSMetadata Source # 
Generic RDSMetadata Source # 

Associated Types

type Rep RDSMetadata :: * -> * #

FromJSON RDSMetadata Source # 
Hashable RDSMetadata Source # 
NFData RDSMetadata Source # 

Methods

rnf :: RDSMetadata -> () #

type Rep RDSMetadata Source # 
type Rep RDSMetadata = D1 (MetaData "RDSMetadata" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.4.3-DbYWzKGDN8K1aNJlQiNb67" False) (C1 (MetaCons "RDSMetadata'" PrefixI True) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_rmSelectSqlQuery") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) ((:*:) (S1 (MetaSel (Just Symbol "_rmDataPipelineId") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) (S1 (MetaSel (Just Symbol "_rmDatabase") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe RDSDatabase))))) ((:*:) (S1 (MetaSel (Just Symbol "_rmDatabaseUserName") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) ((:*:) (S1 (MetaSel (Just Symbol "_rmResourceRole") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) (S1 (MetaSel (Just Symbol "_rmServiceRole") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)))))))

rdsMetadata :: RDSMetadata Source #

Creates a value of RDSMetadata with the minimum fields required to make a request.

Use one of the following lenses to modify other fields as desired:

rmSelectSqlQuery :: Lens' RDSMetadata (Maybe Text) Source #

The SQL query that is supplied during CreateDataSourceFromRDS. Returns only if Verbose is true in GetDataSourceInput.

rmDataPipelineId :: Lens' RDSMetadata (Maybe Text) Source #

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.

rmDatabase :: Lens' RDSMetadata (Maybe RDSDatabase) Source #

The database details required to connect to an Amazon RDS.

rmResourceRole :: Lens' RDSMetadata (Maybe Text) Source #

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 Role templates for data pipelines.

rmServiceRole :: Lens' RDSMetadata (Maybe Text) Source #

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 Role templates for data pipelines.

RealtimeEndpointInfo

data RealtimeEndpointInfo Source #

Describes the real-time endpoint information for an MLModel.

See: realtimeEndpointInfo smart constructor.

Instances

Eq RealtimeEndpointInfo Source # 
Data RealtimeEndpointInfo Source # 

Methods

gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> RealtimeEndpointInfo -> c RealtimeEndpointInfo #

gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c RealtimeEndpointInfo #

toConstr :: RealtimeEndpointInfo -> Constr #

dataTypeOf :: RealtimeEndpointInfo -> DataType #

dataCast1 :: Typeable (* -> *) t => (forall d. Data d => c (t d)) -> Maybe (c RealtimeEndpointInfo) #

dataCast2 :: Typeable (* -> * -> *) t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c RealtimeEndpointInfo) #

gmapT :: (forall b. Data b => b -> b) -> RealtimeEndpointInfo -> RealtimeEndpointInfo #

gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> RealtimeEndpointInfo -> r #

gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> RealtimeEndpointInfo -> r #

gmapQ :: (forall d. Data d => d -> u) -> RealtimeEndpointInfo -> [u] #

gmapQi :: Int -> (forall d. Data d => d -> u) -> RealtimeEndpointInfo -> u #

gmapM :: Monad m => (forall d. Data d => d -> m d) -> RealtimeEndpointInfo -> m RealtimeEndpointInfo #

gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> RealtimeEndpointInfo -> m RealtimeEndpointInfo #

gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> RealtimeEndpointInfo -> m RealtimeEndpointInfo #

Read RealtimeEndpointInfo Source # 
Show RealtimeEndpointInfo Source # 
Generic RealtimeEndpointInfo Source # 
FromJSON RealtimeEndpointInfo Source # 
Hashable RealtimeEndpointInfo Source # 
NFData RealtimeEndpointInfo Source # 

Methods

rnf :: RealtimeEndpointInfo -> () #

type Rep RealtimeEndpointInfo Source # 
type Rep RealtimeEndpointInfo = D1 (MetaData "RealtimeEndpointInfo" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.4.3-DbYWzKGDN8K1aNJlQiNb67" False) (C1 (MetaCons "RealtimeEndpointInfo'" PrefixI True) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_reiCreatedAt") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe POSIX))) (S1 (MetaSel (Just Symbol "_reiEndpointURL") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)))) ((:*:) (S1 (MetaSel (Just Symbol "_reiEndpointStatus") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe RealtimeEndpointStatus))) (S1 (MetaSel (Just Symbol "_reiPeakRequestsPerSecond") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Int))))))

realtimeEndpointInfo :: RealtimeEndpointInfo Source #

Creates a value of RealtimeEndpointInfo with the minimum fields required to make a request.

Use one of the following lenses to modify other fields as desired:

reiCreatedAt :: Lens' RealtimeEndpointInfo (Maybe UTCTime) Source #

The time that the request to create the real-time endpoint for the MLModel was received. The time is expressed in epoch time.

reiEndpointURL :: Lens' RealtimeEndpointInfo (Maybe Text) Source #

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.

reiEndpointStatus :: Lens' RealtimeEndpointInfo (Maybe RealtimeEndpointStatus) Source #

The current status of the real-time endpoint for the MLModel. This element can have one of the following values:

  • NONE - Endpoint does not exist or was previously deleted.
  • READY - Endpoint is ready to be used for real-time predictions.
  • UPDATING - Updating/creating the endpoint.

reiPeakRequestsPerSecond :: Lens' RealtimeEndpointInfo (Maybe Int) Source #

The maximum processing rate for the real-time endpoint for MLModel, measured in incoming requests per second.

RedshiftDataSpec

data RedshiftDataSpec Source #

Describes the data specification of an Amazon Redshift DataSource.

See: redshiftDataSpec smart constructor.

Instances

Eq RedshiftDataSpec Source # 
Data RedshiftDataSpec Source # 

Methods

gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> RedshiftDataSpec -> c RedshiftDataSpec #

gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c RedshiftDataSpec #

toConstr :: RedshiftDataSpec -> Constr #

dataTypeOf :: RedshiftDataSpec -> DataType #

dataCast1 :: Typeable (* -> *) t => (forall d. Data d => c (t d)) -> Maybe (c RedshiftDataSpec) #

dataCast2 :: Typeable (* -> * -> *) t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c RedshiftDataSpec) #

gmapT :: (forall b. Data b => b -> b) -> RedshiftDataSpec -> RedshiftDataSpec #

gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> RedshiftDataSpec -> r #

gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> RedshiftDataSpec -> r #

gmapQ :: (forall d. Data d => d -> u) -> RedshiftDataSpec -> [u] #

gmapQi :: Int -> (forall d. Data d => d -> u) -> RedshiftDataSpec -> u #

gmapM :: Monad m => (forall d. Data d => d -> m d) -> RedshiftDataSpec -> m RedshiftDataSpec #

gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> RedshiftDataSpec -> m RedshiftDataSpec #

gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> RedshiftDataSpec -> m RedshiftDataSpec #

Read RedshiftDataSpec Source # 
Show RedshiftDataSpec Source # 
Generic RedshiftDataSpec Source # 
ToJSON RedshiftDataSpec Source # 
Hashable RedshiftDataSpec Source # 
NFData RedshiftDataSpec Source # 

Methods

rnf :: RedshiftDataSpec -> () #

type Rep RedshiftDataSpec Source # 
type Rep RedshiftDataSpec = D1 (MetaData "RedshiftDataSpec" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.4.3-DbYWzKGDN8K1aNJlQiNb67" False) (C1 (MetaCons "RedshiftDataSpec'" PrefixI True) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_rDataSchemaURI") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) ((:*:) (S1 (MetaSel (Just Symbol "_rDataSchema") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) (S1 (MetaSel (Just Symbol "_rDataRearrangement") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))))) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_rDatabaseInformation") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 RedshiftDatabase)) (S1 (MetaSel (Just Symbol "_rSelectSqlQuery") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 Text))) ((:*:) (S1 (MetaSel (Just Symbol "_rDatabaseCredentials") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 RedshiftDatabaseCredentials)) (S1 (MetaSel (Just Symbol "_rS3StagingLocation") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 Text))))))

rDataSchemaURI :: Lens' RedshiftDataSpec (Maybe Text) Source #

Describes the schema location for an Amazon Redshift DataSource.

rDataSchema :: Lens' RedshiftDataSpec (Maybe Text) Source #

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" ] }

rDataRearrangement :: Lens' RedshiftDataSpec (Maybe Text) Source #

Describes the splitting specifications for a DataSource.

rDatabaseInformation :: Lens' RedshiftDataSpec RedshiftDatabase Source #

Describes the DatabaseName and ClusterIdentifier for an Amazon Redshift DataSource.

rSelectSqlQuery :: Lens' RedshiftDataSpec Text Source #

Describes the SQL Query to execute on an Amazon Redshift database for an Amazon Redshift DataSource.

rDatabaseCredentials :: Lens' RedshiftDataSpec RedshiftDatabaseCredentials Source #

Describes AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon Redshift database.

rS3StagingLocation :: Lens' RedshiftDataSpec Text Source #

Describes an Amazon S3 location to store the result set of the SelectSqlQuery query.

RedshiftDatabase

data RedshiftDatabase Source #

Describes the database details required to connect to an Amazon Redshift database.

See: redshiftDatabase smart constructor.

Instances

Eq RedshiftDatabase Source # 
Data RedshiftDatabase Source # 

Methods

gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> RedshiftDatabase -> c RedshiftDatabase #

gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c RedshiftDatabase #

toConstr :: RedshiftDatabase -> Constr #

dataTypeOf :: RedshiftDatabase -> DataType #

dataCast1 :: Typeable (* -> *) t => (forall d. Data d => c (t d)) -> Maybe (c RedshiftDatabase) #

dataCast2 :: Typeable (* -> * -> *) t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c RedshiftDatabase) #

gmapT :: (forall b. Data b => b -> b) -> RedshiftDatabase -> RedshiftDatabase #

gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> RedshiftDatabase -> r #

gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> RedshiftDatabase -> r #

gmapQ :: (forall d. Data d => d -> u) -> RedshiftDatabase -> [u] #

gmapQi :: Int -> (forall d. Data d => d -> u) -> RedshiftDatabase -> u #

gmapM :: Monad m => (forall d. Data d => d -> m d) -> RedshiftDatabase -> m RedshiftDatabase #

gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> RedshiftDatabase -> m RedshiftDatabase #

gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> RedshiftDatabase -> m RedshiftDatabase #

Read RedshiftDatabase Source # 
Show RedshiftDatabase Source # 
Generic RedshiftDatabase Source # 
ToJSON RedshiftDatabase Source # 
FromJSON RedshiftDatabase Source # 
Hashable RedshiftDatabase Source # 
NFData RedshiftDatabase Source # 

Methods

rnf :: RedshiftDatabase -> () #

type Rep RedshiftDatabase Source # 
type Rep RedshiftDatabase = D1 (MetaData "RedshiftDatabase" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.4.3-DbYWzKGDN8K1aNJlQiNb67" False) (C1 (MetaCons "RedshiftDatabase'" PrefixI True) ((:*:) (S1 (MetaSel (Just Symbol "_rdDatabaseName") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 Text)) (S1 (MetaSel (Just Symbol "_rdClusterIdentifier") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 Text))))

redshiftDatabase Source #

Creates a value of RedshiftDatabase with the minimum fields required to make a request.

Use one of the following lenses to modify other fields as desired:

RedshiftDatabaseCredentials

data RedshiftDatabaseCredentials Source #

Describes the database credentials for connecting to a database on an Amazon Redshift cluster.

See: redshiftDatabaseCredentials smart constructor.

Instances

Eq RedshiftDatabaseCredentials Source # 
Data RedshiftDatabaseCredentials Source # 

Methods

gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> RedshiftDatabaseCredentials -> c RedshiftDatabaseCredentials #

gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c RedshiftDatabaseCredentials #

toConstr :: RedshiftDatabaseCredentials -> Constr #

dataTypeOf :: RedshiftDatabaseCredentials -> DataType #

dataCast1 :: Typeable (* -> *) t => (forall d. Data d => c (t d)) -> Maybe (c RedshiftDatabaseCredentials) #

dataCast2 :: Typeable (* -> * -> *) t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c RedshiftDatabaseCredentials) #

gmapT :: (forall b. Data b => b -> b) -> RedshiftDatabaseCredentials -> RedshiftDatabaseCredentials #

gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> RedshiftDatabaseCredentials -> r #

gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> RedshiftDatabaseCredentials -> r #

gmapQ :: (forall d. Data d => d -> u) -> RedshiftDatabaseCredentials -> [u] #

gmapQi :: Int -> (forall d. Data d => d -> u) -> RedshiftDatabaseCredentials -> u #

gmapM :: Monad m => (forall d. Data d => d -> m d) -> RedshiftDatabaseCredentials -> m RedshiftDatabaseCredentials #

gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> RedshiftDatabaseCredentials -> m RedshiftDatabaseCredentials #

gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> RedshiftDatabaseCredentials -> m RedshiftDatabaseCredentials #

Read RedshiftDatabaseCredentials Source # 
Show RedshiftDatabaseCredentials Source # 
Generic RedshiftDatabaseCredentials Source # 
ToJSON RedshiftDatabaseCredentials Source # 
Hashable RedshiftDatabaseCredentials Source # 
NFData RedshiftDatabaseCredentials Source # 
type Rep RedshiftDatabaseCredentials Source # 
type Rep RedshiftDatabaseCredentials = D1 (MetaData "RedshiftDatabaseCredentials" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.4.3-DbYWzKGDN8K1aNJlQiNb67" False) (C1 (MetaCons "RedshiftDatabaseCredentials'" PrefixI True) ((:*:) (S1 (MetaSel (Just Symbol "_rdcUsername") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 Text)) (S1 (MetaSel (Just Symbol "_rdcPassword") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 Text))))

redshiftDatabaseCredentials Source #

Creates a value of RedshiftDatabaseCredentials with the minimum fields required to make a request.

Use one of the following lenses to modify other fields as desired:

RedshiftMetadata

data RedshiftMetadata Source #

Describes the DataSource details specific to Amazon Redshift.

See: redshiftMetadata smart constructor.

Instances

Eq RedshiftMetadata Source # 
Data RedshiftMetadata Source # 

Methods

gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> RedshiftMetadata -> c RedshiftMetadata #

gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c RedshiftMetadata #

toConstr :: RedshiftMetadata -> Constr #

dataTypeOf :: RedshiftMetadata -> DataType #

dataCast1 :: Typeable (* -> *) t => (forall d. Data d => c (t d)) -> Maybe (c RedshiftMetadata) #

dataCast2 :: Typeable (* -> * -> *) t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c RedshiftMetadata) #

gmapT :: (forall b. Data b => b -> b) -> RedshiftMetadata -> RedshiftMetadata #

gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> RedshiftMetadata -> r #

gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> RedshiftMetadata -> r #

gmapQ :: (forall d. Data d => d -> u) -> RedshiftMetadata -> [u] #

gmapQi :: Int -> (forall d. Data d => d -> u) -> RedshiftMetadata -> u #

gmapM :: Monad m => (forall d. Data d => d -> m d) -> RedshiftMetadata -> m RedshiftMetadata #

gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> RedshiftMetadata -> m RedshiftMetadata #

gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> RedshiftMetadata -> m RedshiftMetadata #

Read RedshiftMetadata Source # 
Show RedshiftMetadata Source # 
Generic RedshiftMetadata Source # 
FromJSON RedshiftMetadata Source # 
Hashable RedshiftMetadata Source # 
NFData RedshiftMetadata Source # 

Methods

rnf :: RedshiftMetadata -> () #

type Rep RedshiftMetadata Source # 
type Rep RedshiftMetadata = D1 (MetaData "RedshiftMetadata" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.4.3-DbYWzKGDN8K1aNJlQiNb67" False) (C1 (MetaCons "RedshiftMetadata'" PrefixI True) ((:*:) (S1 (MetaSel (Just Symbol "_redSelectSqlQuery") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) ((:*:) (S1 (MetaSel (Just Symbol "_redRedshiftDatabase") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe RedshiftDatabase))) (S1 (MetaSel (Just Symbol "_redDatabaseUserName") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))))))

redshiftMetadata :: RedshiftMetadata Source #

Creates a value of RedshiftMetadata with the minimum fields required to make a request.

Use one of the following lenses to modify other fields as desired:

redSelectSqlQuery :: Lens' RedshiftMetadata (Maybe Text) Source #

The SQL query that is specified during CreateDataSourceFromRedshift. Returns only if Verbose is true in GetDataSourceInput.

S3DataSpec

data S3DataSpec Source #

Describes the data specification of a DataSource.

See: s3DataSpec smart constructor.

Instances

Eq S3DataSpec Source # 
Data S3DataSpec Source # 

Methods

gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> S3DataSpec -> c S3DataSpec #

gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c S3DataSpec #

toConstr :: S3DataSpec -> Constr #

dataTypeOf :: S3DataSpec -> DataType #

dataCast1 :: Typeable (* -> *) t => (forall d. Data d => c (t d)) -> Maybe (c S3DataSpec) #

dataCast2 :: Typeable (* -> * -> *) t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c S3DataSpec) #

gmapT :: (forall b. Data b => b -> b) -> S3DataSpec -> S3DataSpec #

gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> S3DataSpec -> r #

gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> S3DataSpec -> r #

gmapQ :: (forall d. Data d => d -> u) -> S3DataSpec -> [u] #

gmapQi :: Int -> (forall d. Data d => d -> u) -> S3DataSpec -> u #

gmapM :: Monad m => (forall d. Data d => d -> m d) -> S3DataSpec -> m S3DataSpec #

gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> S3DataSpec -> m S3DataSpec #

gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> S3DataSpec -> m S3DataSpec #

Read S3DataSpec Source # 
Show S3DataSpec Source # 
Generic S3DataSpec Source # 

Associated Types

type Rep S3DataSpec :: * -> * #

ToJSON S3DataSpec Source # 
Hashable S3DataSpec Source # 
NFData S3DataSpec Source # 

Methods

rnf :: S3DataSpec -> () #

type Rep S3DataSpec Source # 
type Rep S3DataSpec = D1 (MetaData "S3DataSpec" "Network.AWS.MachineLearning.Types.Product" "amazonka-ml-1.4.3-DbYWzKGDN8K1aNJlQiNb67" False) (C1 (MetaCons "S3DataSpec'" PrefixI True) ((:*:) ((:*:) (S1 (MetaSel (Just Symbol "_sdsDataSchema") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) (S1 (MetaSel (Just Symbol "_sdsDataSchemaLocationS3") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text)))) ((:*:) (S1 (MetaSel (Just Symbol "_sdsDataRearrangement") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 (Maybe Text))) (S1 (MetaSel (Just Symbol "_sdsDataLocationS3") NoSourceUnpackedness SourceStrict DecidedStrict) (Rec0 Text)))))

s3DataSpec Source #

Creates a value of S3DataSpec with the minimum fields required to make a request.

Use one of the following lenses to modify other fields as desired:

sdsDataSchema :: Lens' S3DataSpec (Maybe Text) Source #

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.

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" ] }

sdsDataSchemaLocationS3 :: Lens' S3DataSpec (Maybe Text) Source #

Describes the schema Location in Amazon S3.

sdsDataRearrangement :: Lens' S3DataSpec (Maybe Text) Source #

Describes the splitting requirement of a Datasource.

sdsDataLocationS3 :: Lens' S3DataSpec Text Source #

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.