/ 
Paws-0.05
River stage two • 9 direct dependents • 11 total dependents
/ Paws::MachineLearning

NAME

Paws::MachineLearning - Perl Interface to AWS Amazon Machine Learning

SYNOPSIS

use Paws;

my $obj = Paws->service('MachineLearning')->new;
my $res = $obj->Method(
  Arg1 => $val1,
  Arg2 => [ 'V1', 'V2' ],
  # if Arg3 is an object, the HashRef will be used as arguments to the constructor
  # of the arguments type
  Arg3 => { Att1 => 'Val1' },
  # if Arg4 is an array of objects, the HashRefs will be passed as arguments to
  # the constructor of the arguments type
  Arg4 => [ { Att1 => 'Val1'  }, { Att1 => 'Val2' } ],
);

DESCRIPTION

Definition of the public APIs exposed by Amazon Machine Learning

METHODS

CreateBatchPrediction(BatchPredictionDataSourceId => Str, BatchPredictionId => Str, MLModelId => Str, OutputUri => Str, [BatchPredictionName => Str])

Each argument is described in detail in: Paws::MachineLearning::CreateBatchPrediction

Returns: a Paws::MachineLearning::CreateBatchPredictionOutput instance

Generates predictions for a group of observations. The observations to process exist in one or more data files referenced by a DataSource. This operation creates a new BatchPrediction, and uses an MLModel and the data files referenced by the DataSource as information sources.

CreateBatchPrediction is an asynchronous operation. In response to CreateBatchPrediction, Amazon Machine Learning (Amazon ML) immediately returns and sets the BatchPrediction status to PENDING. After the BatchPrediction completes, Amazon ML sets the status to COMPLETED.

You can poll for status updates by using the GetBatchPrediction operation and checking the Status parameter of the result. After the COMPLETED status appears, the results are available in the location specified by the OutputUri parameter.

CreateDataSourceFromRDS(DataSourceId => Str, RDSData => Paws::MachineLearning::RDSDataSpec, RoleARN => Str, [ComputeStatistics => Bool, DataSourceName => Str])

Each argument is described in detail in: Paws::MachineLearning::CreateDataSourceFromRDS

Returns: a Paws::MachineLearning::CreateDataSourceFromRDSOutput instance

Creates a DataSource object from an Amazon Relational Database Service (Amazon RDS). A DataSource references data that can be used to perform CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations.

CreateDataSourceFromRDS is an asynchronous operation. In response to CreateDataSourceFromRDS, Amazon Machine Learning (Amazon ML) immediately returns and sets the DataSource status to PENDING. After the DataSource is created and ready for use, Amazon ML sets the Status parameter to COMPLETED. DataSource in COMPLETED or PENDING status can only be used to perform CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations.

If Amazon ML cannot accept the input source, it sets the Status parameter to FAILED and includes an error message in the Message attribute of the GetDataSource operation response.

CreateDataSourceFromRedshift(DataSourceId => Str, DataSpec => Paws::MachineLearning::RedshiftDataSpec, RoleARN => Str, [ComputeStatistics => Bool, DataSourceName => Str])

Each argument is described in detail in: Paws::MachineLearning::CreateDataSourceFromRedshift

Returns: a Paws::MachineLearning::CreateDataSourceFromRedshiftOutput instance

Creates a DataSource from Amazon Redshift. A DataSource references data that can be used to perform either CreateMLModel, CreateEvaluation or CreateBatchPrediction operations.

CreateDataSourceFromRedshift is an asynchronous operation. In response to CreateDataSourceFromRedshift, Amazon Machine Learning (Amazon ML) immediately returns and sets the DataSource status to PENDING. After the DataSource is created and ready for use, Amazon ML sets the Status parameter to COMPLETED. DataSource in COMPLETED or PENDING status can only be used to perform CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations.

If Amazon ML cannot accept the input source, it sets the Status parameter to FAILED and includes an error message in the Message attribute of the GetDataSource operation response.

The observations should exist in the database hosted on an Amazon Redshift cluster and should be specified by a SelectSqlQuery. Amazon ML executes Unload command in Amazon Redshift to transfer the result set of SelectSqlQuery to S3StagingLocation.

After the DataSource is created, it's ready for use in evaluations and batch predictions. If you plan to use the DataSource to train an MLModel, the DataSource requires another item -- a recipe. A recipe describes the observation variables that participate in training an MLModel. A recipe describes how each input variable will be used in training. Will the variable be included or excluded from training? Will the variable be manipulated, for example, combined with another variable or split apart into word combinations? The recipe provides answers to these questions. For more information, see the Amazon Machine Learning Developer Guide.

CreateDataSourceFromS3(DataSourceId => Str, DataSpec => Paws::MachineLearning::S3DataSpec, [ComputeStatistics => Bool, DataSourceName => Str])

Each argument is described in detail in: Paws::MachineLearning::CreateDataSourceFromS3

Returns: a Paws::MachineLearning::CreateDataSourceFromS3Output instance

Creates a DataSource object. A DataSource references data that can be used to perform CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations.

CreateDataSourceFromS3 is an asynchronous operation. In response to CreateDataSourceFromS3, Amazon Machine Learning (Amazon ML) immediately returns and sets the DataSource status to PENDING. After the DataSource is created and ready for use, Amazon ML sets the Status parameter to COMPLETED. DataSource in COMPLETED or PENDING status can only be used to perform CreateMLModel, CreateEvaluation or CreateBatchPrediction operations.

If Amazon ML cannot accept the input source, it sets the Status parameter to FAILED and includes an error message in the Message attribute of the GetDataSource operation response.

The observation data used in a DataSource should be ready to use; that is, it should have a consistent structure, and missing data values should be kept to a minimum. The observation data must reside in one or more CSV files in an Amazon Simple Storage Service (Amazon S3) bucket, along with a schema that describes the data items by name and type. The same schema must be used for all of the data files referenced by the DataSource.

After the DataSource has been created, it's ready to use in evaluations and batch predictions. If you plan to use the DataSource to train an MLModel, the DataSource requires another item: a recipe. A recipe describes the observation variables that participate in training an MLModel. A recipe describes how each input variable will be used in training. Will the variable be included or excluded from training? Will the variable be manipulated, for example, combined with another variable, or split apart into word combinations? The recipe provides answers to these questions. For more information, see the Amazon Machine Learning Developer Guide.

CreateEvaluation(EvaluationDataSourceId => Str, EvaluationId => Str, MLModelId => Str, [EvaluationName => Str])

Each argument is described in detail in: Paws::MachineLearning::CreateEvaluation

Returns: a Paws::MachineLearning::CreateEvaluationOutput instance

Creates a new Evaluation of an MLModel. An MLModel is evaluated on a set of observations associated to a DataSource. Like a DataSource for an MLModel, the DataSource for an Evaluation contains values for the Target Variable. The Evaluation compares the predicted result for each observation to the actual outcome and provides a summary so that you know how effective the MLModel functions on the test data. Evaluation generates a relevant performance metric such as BinaryAUC, RegressionRMSE or MulticlassAvgFScore based on the corresponding MLModelType: BINARY, REGRESSION or MULTICLASS.

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

You can use the GetEvaluation operation to check progress of the evaluation during the creation operation.

CreateMLModel(MLModelId => Str, MLModelType => Str, TrainingDataSourceId => Str, [MLModelName => Str, Parameters => Paws::MachineLearning::TrainingParameters, Recipe => Str, RecipeUri => Str])

Each argument is described in detail in: Paws::MachineLearning::CreateMLModel

Returns: a Paws::MachineLearning::CreateMLModelOutput instance

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

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

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

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

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

CreateRealtimeEndpoint(MLModelId => Str)

Each argument is described in detail in: Paws::MachineLearning::CreateRealtimeEndpoint

Returns: a Paws::MachineLearning::CreateRealtimeEndpointOutput instance

Creates a real-time endpoint for the MLModel. The endpoint contains the URI of the MLModel; that is, the location to send real-time prediction requests for the specified MLModel.

DeleteBatchPrediction(BatchPredictionId => Str)

Each argument is described in detail in: Paws::MachineLearning::DeleteBatchPrediction

Returns: a Paws::MachineLearning::DeleteBatchPredictionOutput instance

Assigns the DELETED status to a BatchPrediction, rendering it unusable.

After using the DeleteBatchPrediction operation, you can use the GetBatchPrediction operation to verify that the status of the BatchPrediction changed to DELETED.

The result of the DeleteBatchPrediction operation is irreversible.

DeleteDataSource(DataSourceId => Str)

Each argument is described in detail in: Paws::MachineLearning::DeleteDataSource

Returns: a Paws::MachineLearning::DeleteDataSourceOutput instance

Assigns the DELETED status to a DataSource, rendering it unusable.

After using the DeleteDataSource operation, you can use the GetDataSource operation to verify that the status of the DataSource changed to DELETED.

The results of the DeleteDataSource operation are irreversible.

DeleteEvaluation(EvaluationId => Str)

Each argument is described in detail in: Paws::MachineLearning::DeleteEvaluation

Returns: a Paws::MachineLearning::DeleteEvaluationOutput instance

Assigns the DELETED status to an Evaluation, rendering it unusable.

After invoking the DeleteEvaluation operation, you can use the GetEvaluation operation to verify that the status of the Evaluation changed to DELETED.

The results of the DeleteEvaluation operation are irreversible.

DeleteMLModel(MLModelId => Str)

Each argument is described in detail in: Paws::MachineLearning::DeleteMLModel

Returns: a Paws::MachineLearning::DeleteMLModelOutput instance

Assigns the DELETED status to an MLModel, rendering it unusable.

After using the DeleteMLModel operation, you can use the GetMLModel operation to verify that the status of the MLModel changed to DELETED.

The result of the DeleteMLModel operation is irreversible.

DeleteRealtimeEndpoint(MLModelId => Str)

Each argument is described in detail in: Paws::MachineLearning::DeleteRealtimeEndpoint

Returns: a Paws::MachineLearning::DeleteRealtimeEndpointOutput instance

Deletes a real time endpoint of an MLModel.

DescribeBatchPredictions([EQ => Str, FilterVariable => Str, GE => Str, GT => Str, LE => Str, Limit => Int, LT => Str, NE => Str, NextToken => Str, Prefix => Str, SortOrder => Str])

Each argument is described in detail in: Paws::MachineLearning::DescribeBatchPredictions

Returns: a Paws::MachineLearning::DescribeBatchPredictionsOutput instance

Returns a list of BatchPrediction operations that match the search criteria in the request.

DescribeDataSources([EQ => Str, FilterVariable => Str, GE => Str, GT => Str, LE => Str, Limit => Int, LT => Str, NE => Str, NextToken => Str, Prefix => Str, SortOrder => Str])

Each argument is described in detail in: Paws::MachineLearning::DescribeDataSources

Returns: a Paws::MachineLearning::DescribeDataSourcesOutput instance

Returns a list of DataSource that match the search criteria in the request.

DescribeEvaluations([EQ => Str, FilterVariable => Str, GE => Str, GT => Str, LE => Str, Limit => Int, LT => Str, NE => Str, NextToken => Str, Prefix => Str, SortOrder => Str])

Each argument is described in detail in: Paws::MachineLearning::DescribeEvaluations

Returns: a Paws::MachineLearning::DescribeEvaluationsOutput instance

Returns a list of DescribeEvaluations that match the search criteria in the request.

DescribeMLModels([EQ => Str, FilterVariable => Str, GE => Str, GT => Str, LE => Str, Limit => Int, LT => Str, NE => Str, NextToken => Str, Prefix => Str, SortOrder => Str])

Each argument is described in detail in: Paws::MachineLearning::DescribeMLModels

Returns: a Paws::MachineLearning::DescribeMLModelsOutput instance

Returns a list of MLModel that match the search criteria in the request.

GetBatchPrediction(BatchPredictionId => Str)

Each argument is described in detail in: Paws::MachineLearning::GetBatchPrediction

Returns: a Paws::MachineLearning::GetBatchPredictionOutput instance

Returns a BatchPrediction that includes detailed metadata, status, and data file information for a Batch Prediction request.

GetDataSource(DataSourceId => Str, [Verbose => Bool])

Each argument is described in detail in: Paws::MachineLearning::GetDataSource

Returns: a Paws::MachineLearning::GetDataSourceOutput instance

Returns a DataSource that includes metadata and data file information, as well as the current status of the DataSource.

GetDataSource provides results in normal or verbose format. The verbose format adds the schema description and the list of files pointed to by the DataSource to the normal format.

GetEvaluation(EvaluationId => Str)

Each argument is described in detail in: Paws::MachineLearning::GetEvaluation

Returns: a Paws::MachineLearning::GetEvaluationOutput instance

Returns an Evaluation that includes metadata as well as the current status of the Evaluation.

GetMLModel(MLModelId => Str, [Verbose => Bool])

Each argument is described in detail in: Paws::MachineLearning::GetMLModel

Returns: a Paws::MachineLearning::GetMLModelOutput instance

Returns an MLModel that includes detailed metadata, and data source information as well as the current status of the MLModel.

GetMLModel provides results in normal or verbose format.

Predict(MLModelId => Str, PredictEndpoint => Str, Record => Paws::MachineLearning::Record)

Each argument is described in detail in: Paws::MachineLearning::Predict

Returns: a Paws::MachineLearning::PredictOutput instance

Generates a prediction for the observation using the specified MLModel.

Not all response parameters will be populated because this is dependent on the type of requested model.

UpdateBatchPrediction(BatchPredictionId => Str, BatchPredictionName => Str)

Each argument is described in detail in: Paws::MachineLearning::UpdateBatchPrediction

Returns: a Paws::MachineLearning::UpdateBatchPredictionOutput instance

Updates the BatchPredictionName of a BatchPrediction.

You can use the GetBatchPrediction operation to view the contents of the updated data element.

UpdateDataSource(DataSourceId => Str, DataSourceName => Str)

Each argument is described in detail in: Paws::MachineLearning::UpdateDataSource

Returns: a Paws::MachineLearning::UpdateDataSourceOutput instance

Updates the DataSourceName of a DataSource.

You can use the GetDataSource operation to view the contents of the updated data element.

UpdateEvaluation(EvaluationId => Str, EvaluationName => Str)

Each argument is described in detail in: Paws::MachineLearning::UpdateEvaluation

Returns: a Paws::MachineLearning::UpdateEvaluationOutput instance

Updates the EvaluationName of an Evaluation.

You can use the GetEvaluation operation to view the contents of the updated data element.

UpdateMLModel(MLModelId => Str, [MLModelName => Str, ScoreThreshold => Num])

Each argument is described in detail in: Paws::MachineLearning::UpdateMLModel

Returns: a Paws::MachineLearning::UpdateMLModelOutput instance

Updates the MLModelName and the ScoreThreshold of an MLModel.

You can use the GetMLModel operation to view the contents of the updated data element.

SEE ALSO

This service class forms part of Paws

BUGS and CONTRIBUTIONS

The source code is located here: https://github.com/pplu/aws-sdk-perl

Please report bugs to: https://github.com/pplu/aws-sdk-perl/issues