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Paws::MachineLearning(3) User Contributed Perl Documentation Paws::MachineLearning(3)

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

  use Paws;
  my $obj = Paws->service('MachineLearning');
  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' } ],
  );

Definition of the public APIs exposed by Amazon Machine Learning

For the AWS API documentation, see <https://docs.aws.amazon.com/goto/WebAPI/machinelearning-2014-12-12>

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

Returns: a Paws::MachineLearning::AddTagsOutput instance

Adds one or more tags to an object, up to a limit of 10. Each tag consists of a key and an optional value. If you add a tag using a key that is already associated with the ML object, "AddTags" updates the tag's value.

[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.

[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 (http://aws.amazon.com/rds/) (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 the "COMPLETED" or "PENDING" state can be used only 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.

[ComputeStatistics => Bool]
[DataSourceName => Str]

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

Returns: a Paws::MachineLearning::CreateDataSourceFromRedshiftOutput instance

Creates a "DataSource" from a database hosted on an Amazon Redshift cluster. 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" states can be used to perform only "CreateMLModel", "CreateEvaluation", or "CreateBatchPrediction" operations.

If Amazon ML can't 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 be contained in the database hosted on an Amazon Redshift cluster and should be specified by a "SelectSqlQuery" query. Amazon ML executes an "Unload" command in Amazon Redshift to transfer the result set of the "SelectSqlQuery" query to "S3StagingLocation".

After the "DataSource" has been created, it's ready for use in evaluations and batch predictions. If you plan to use the "DataSource" to train an "MLModel", the "DataSource" also requires a recipe. A recipe describes how each input variable will be used in training an "MLModel". Will the variable be included or excluded from training? Will the variable be manipulated; for example, will it be combined with another variable or will it be split apart into word combinations? The recipe provides answers to these questions.

You can't change an existing datasource, but you can copy and modify the settings from an existing Amazon Redshift datasource to create a new datasource. To do so, call "GetDataSource" for an existing datasource and copy the values to a "CreateDataSource" call. Change the settings that you want to change and make sure that all required fields have the appropriate values.

[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" has been created and is ready for use, Amazon ML sets the "Status" parameter to "COMPLETED". "DataSource" in the "COMPLETED" or "PENDING" state can be used to perform only "CreateMLModel", "CreateEvaluation" or "CreateBatchPrediction" operations.

If Amazon ML can't 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) location, 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" also needs a recipe. A recipe describes how each input variable will be used in training an "MLModel". Will the variable be included or excluded from training? Will the variable be manipulated; for example, will it be combined with another variable or will it be split apart into word combinations? The recipe provides answers to these questions.

[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.

[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 "DataSource" and the recipe as information sources.

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

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

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

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

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".

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.

Caution: The result of the "DeleteBatchPrediction" operation is irreversible.

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.

Caution: The results of the "DeleteDataSource" operation are irreversible.

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".

Caution: The results of the "DeleteEvaluation" operation are irreversible.

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.

Caution: The result of the "DeleteMLModel" operation is irreversible.

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

Returns: a Paws::MachineLearning::DeleteRealtimeEndpointOutput instance

Deletes a real time endpoint of an "MLModel".

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

Returns: a Paws::MachineLearning::DeleteTagsOutput instance

Deletes the specified tags associated with an ML object. After this operation is complete, you can't recover deleted tags.

If you specify a tag that doesn't exist, Amazon ML ignores it.

[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.

[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.

[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.

[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.

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

Returns: a Paws::MachineLearning::DescribeTagsOutput instance

Describes one or more of the tags for your Amazon ML object.

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.

[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.

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".

[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, data source information, and the current status of the "MLModel".

"GetMLModel" provides results in normal or verbose format.

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 "ML Model".

Note: Not all response parameters will be populated. Whether a response parameter is populated depends on the type of model requested.

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.

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.

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.

[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.

Paginator methods are helpers that repetively call methods that return partial results

If passed a sub as first parameter, it will call the sub for each element found in :

 - Results, passing the object as the first parameter, and the string 'Results' as the second parameter

If not, it will return a a Paws::MachineLearning::DescribeBatchPredictionsOutput instance with all the "param"s; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory.

If passed a sub as first parameter, it will call the sub for each element found in :

 - Results, passing the object as the first parameter, and the string 'Results' as the second parameter

If not, it will return a a Paws::MachineLearning::DescribeDataSourcesOutput instance with all the "param"s; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory.

If passed a sub as first parameter, it will call the sub for each element found in :

 - Results, passing the object as the first parameter, and the string 'Results' as the second parameter

If not, it will return a a Paws::MachineLearning::DescribeEvaluationsOutput instance with all the "param"s; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory.

If passed a sub as first parameter, it will call the sub for each element found in :

 - Results, passing the object as the first parameter, and the string 'Results' as the second parameter

If not, it will return a a Paws::MachineLearning::DescribeMLModelsOutput instance with all the "param"s; from all the responses. Please take into account that this mode can potentially consume vasts ammounts of memory.

This service class forms part of Paws

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>

2022-06-01 perl v5.40.2
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