/** * Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. * SPDX-License-Identifier: Apache-2.0. */ #pragma once #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include namespace Aws { namespace Http { class HttpClient; class HttpClientFactory; } // namespace Http namespace Utils { template< typename R, typename E> class Outcome; namespace Threading { class Executor; } // namespace Threading } // namespace Utils namespace Auth { class AWSCredentials; class AWSCredentialsProvider; } // namespace Auth namespace Client { class RetryStrategy; } // namespace Client namespace MachineLearning { namespace Model { class AddTagsRequest; class CreateBatchPredictionRequest; class CreateDataSourceFromRDSRequest; class CreateDataSourceFromRedshiftRequest; class CreateDataSourceFromS3Request; class CreateEvaluationRequest; class CreateMLModelRequest; class CreateRealtimeEndpointRequest; class DeleteBatchPredictionRequest; class DeleteDataSourceRequest; class DeleteEvaluationRequest; class DeleteMLModelRequest; class DeleteRealtimeEndpointRequest; class DeleteTagsRequest; class DescribeBatchPredictionsRequest; class DescribeDataSourcesRequest; class DescribeEvaluationsRequest; class DescribeMLModelsRequest; class DescribeTagsRequest; class GetBatchPredictionRequest; class GetDataSourceRequest; class GetEvaluationRequest; class GetMLModelRequest; class PredictRequest; class UpdateBatchPredictionRequest; class UpdateDataSourceRequest; class UpdateEvaluationRequest; class UpdateMLModelRequest; typedef Aws::Utils::Outcome AddTagsOutcome; typedef Aws::Utils::Outcome CreateBatchPredictionOutcome; typedef Aws::Utils::Outcome CreateDataSourceFromRDSOutcome; typedef Aws::Utils::Outcome CreateDataSourceFromRedshiftOutcome; typedef Aws::Utils::Outcome CreateDataSourceFromS3Outcome; typedef Aws::Utils::Outcome CreateEvaluationOutcome; typedef Aws::Utils::Outcome CreateMLModelOutcome; typedef Aws::Utils::Outcome CreateRealtimeEndpointOutcome; typedef Aws::Utils::Outcome DeleteBatchPredictionOutcome; typedef Aws::Utils::Outcome DeleteDataSourceOutcome; typedef Aws::Utils::Outcome DeleteEvaluationOutcome; typedef Aws::Utils::Outcome DeleteMLModelOutcome; typedef Aws::Utils::Outcome DeleteRealtimeEndpointOutcome; typedef Aws::Utils::Outcome DeleteTagsOutcome; typedef Aws::Utils::Outcome DescribeBatchPredictionsOutcome; typedef Aws::Utils::Outcome DescribeDataSourcesOutcome; typedef Aws::Utils::Outcome DescribeEvaluationsOutcome; typedef Aws::Utils::Outcome DescribeMLModelsOutcome; typedef Aws::Utils::Outcome DescribeTagsOutcome; typedef Aws::Utils::Outcome GetBatchPredictionOutcome; typedef Aws::Utils::Outcome GetDataSourceOutcome; typedef Aws::Utils::Outcome GetEvaluationOutcome; typedef Aws::Utils::Outcome GetMLModelOutcome; typedef Aws::Utils::Outcome PredictOutcome; typedef Aws::Utils::Outcome UpdateBatchPredictionOutcome; typedef Aws::Utils::Outcome UpdateDataSourceOutcome; typedef Aws::Utils::Outcome UpdateEvaluationOutcome; typedef Aws::Utils::Outcome UpdateMLModelOutcome; typedef std::future AddTagsOutcomeCallable; typedef std::future CreateBatchPredictionOutcomeCallable; typedef std::future CreateDataSourceFromRDSOutcomeCallable; typedef std::future CreateDataSourceFromRedshiftOutcomeCallable; typedef std::future CreateDataSourceFromS3OutcomeCallable; typedef std::future CreateEvaluationOutcomeCallable; typedef std::future CreateMLModelOutcomeCallable; typedef std::future CreateRealtimeEndpointOutcomeCallable; typedef std::future DeleteBatchPredictionOutcomeCallable; typedef std::future DeleteDataSourceOutcomeCallable; typedef std::future DeleteEvaluationOutcomeCallable; typedef std::future DeleteMLModelOutcomeCallable; typedef std::future DeleteRealtimeEndpointOutcomeCallable; typedef std::future DeleteTagsOutcomeCallable; typedef std::future DescribeBatchPredictionsOutcomeCallable; typedef std::future DescribeDataSourcesOutcomeCallable; typedef std::future DescribeEvaluationsOutcomeCallable; typedef std::future DescribeMLModelsOutcomeCallable; typedef std::future DescribeTagsOutcomeCallable; typedef std::future GetBatchPredictionOutcomeCallable; typedef std::future GetDataSourceOutcomeCallable; typedef std::future GetEvaluationOutcomeCallable; typedef std::future GetMLModelOutcomeCallable; typedef std::future PredictOutcomeCallable; typedef std::future UpdateBatchPredictionOutcomeCallable; typedef std::future UpdateDataSourceOutcomeCallable; typedef std::future UpdateEvaluationOutcomeCallable; typedef std::future UpdateMLModelOutcomeCallable; } // namespace Model class MachineLearningClient; typedef std::function&) > AddTagsResponseReceivedHandler; typedef std::function&) > CreateBatchPredictionResponseReceivedHandler; typedef std::function&) > CreateDataSourceFromRDSResponseReceivedHandler; typedef std::function&) > CreateDataSourceFromRedshiftResponseReceivedHandler; typedef std::function&) > CreateDataSourceFromS3ResponseReceivedHandler; typedef std::function&) > CreateEvaluationResponseReceivedHandler; typedef std::function&) > CreateMLModelResponseReceivedHandler; typedef std::function&) > CreateRealtimeEndpointResponseReceivedHandler; typedef std::function&) > DeleteBatchPredictionResponseReceivedHandler; typedef std::function&) > DeleteDataSourceResponseReceivedHandler; typedef std::function&) > DeleteEvaluationResponseReceivedHandler; typedef std::function&) > DeleteMLModelResponseReceivedHandler; typedef std::function&) > DeleteRealtimeEndpointResponseReceivedHandler; typedef std::function&) > DeleteTagsResponseReceivedHandler; typedef std::function&) > DescribeBatchPredictionsResponseReceivedHandler; typedef std::function&) > DescribeDataSourcesResponseReceivedHandler; typedef std::function&) > DescribeEvaluationsResponseReceivedHandler; typedef std::function&) > DescribeMLModelsResponseReceivedHandler; typedef std::function&) > DescribeTagsResponseReceivedHandler; typedef std::function&) > GetBatchPredictionResponseReceivedHandler; typedef std::function&) > GetDataSourceResponseReceivedHandler; typedef std::function&) > GetEvaluationResponseReceivedHandler; typedef std::function&) > GetMLModelResponseReceivedHandler; typedef std::function&) > PredictResponseReceivedHandler; typedef std::function&) > UpdateBatchPredictionResponseReceivedHandler; typedef std::function&) > UpdateDataSourceResponseReceivedHandler; typedef std::function&) > UpdateEvaluationResponseReceivedHandler; typedef std::function&) > UpdateMLModelResponseReceivedHandler; /** * Definition of the public APIs exposed by Amazon Machine Learning */ class AWS_MACHINELEARNING_API MachineLearningClient : public Aws::Client::AWSJsonClient { public: typedef Aws::Client::AWSJsonClient BASECLASS; /** * Initializes client to use DefaultCredentialProviderChain, with default http client factory, and optional client config. If client config * is not specified, it will be initialized to default values. */ MachineLearningClient(const Aws::Client::ClientConfiguration& clientConfiguration = Aws::Client::ClientConfiguration()); /** * Initializes client to use SimpleAWSCredentialsProvider, with default http client factory, and optional client config. If client config * is not specified, it will be initialized to default values. */ MachineLearningClient(const Aws::Auth::AWSCredentials& credentials, const Aws::Client::ClientConfiguration& clientConfiguration = Aws::Client::ClientConfiguration()); /** * Initializes client to use specified credentials provider with specified client config. If http client factory is not supplied, * the default http client factory will be used */ MachineLearningClient(const std::shared_ptr& credentialsProvider, const Aws::Client::ClientConfiguration& clientConfiguration = Aws::Client::ClientConfiguration()); virtual ~MachineLearningClient(); /** *

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.

See Also:

AWS * API Reference

*/ virtual Model::AddTagsOutcome AddTags(const Model::AddTagsRequest& request) const; /** *

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.

See Also:

AWS * API Reference

* * returns a future to the operation so that it can be executed in parallel to other requests. */ virtual Model::AddTagsOutcomeCallable AddTagsCallable(const Model::AddTagsRequest& request) const; /** *

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.

See Also:

AWS * API Reference

* * Queues the request into a thread executor and triggers associated callback when operation has finished. */ virtual void AddTagsAsync(const Model::AddTagsRequest& request, const AddTagsResponseReceivedHandler& handler, const std::shared_ptr& context = nullptr) const; /** *

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.

See Also:

AWS * API Reference

*/ virtual Model::CreateBatchPredictionOutcome CreateBatchPrediction(const Model::CreateBatchPredictionRequest& request) const; /** *

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.

See Also:

AWS * API Reference

* * returns a future to the operation so that it can be executed in parallel to other requests. */ virtual Model::CreateBatchPredictionOutcomeCallable CreateBatchPredictionCallable(const Model::CreateBatchPredictionRequest& request) const; /** *

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.

See Also:

AWS * API Reference

* * Queues the request into a thread executor and triggers associated callback when operation has finished. */ virtual void CreateBatchPredictionAsync(const Model::CreateBatchPredictionRequest& request, const CreateBatchPredictionResponseReceivedHandler& handler, const std::shared_ptr& context = nullptr) const; /** *

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 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.

See Also:

AWS * API Reference

*/ virtual Model::CreateDataSourceFromRDSOutcome CreateDataSourceFromRDS(const Model::CreateDataSourceFromRDSRequest& request) const; /** *

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 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.

See Also:

AWS * API Reference

* * returns a future to the operation so that it can be executed in parallel to other requests. */ virtual Model::CreateDataSourceFromRDSOutcomeCallable CreateDataSourceFromRDSCallable(const Model::CreateDataSourceFromRDSRequest& request) const; /** *

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 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.

See Also:

AWS * API Reference

* * Queues the request into a thread executor and triggers associated callback when operation has finished. */ virtual void CreateDataSourceFromRDSAsync(const Model::CreateDataSourceFromRDSRequest& request, const CreateDataSourceFromRDSResponseReceivedHandler& handler, const std::shared_ptr& context = nullptr) const; /** *

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.

See Also:

* AWS * API Reference

*/ virtual Model::CreateDataSourceFromRedshiftOutcome CreateDataSourceFromRedshift(const Model::CreateDataSourceFromRedshiftRequest& request) const; /** *

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.

See Also:

* AWS * API Reference

* * returns a future to the operation so that it can be executed in parallel to other requests. */ virtual Model::CreateDataSourceFromRedshiftOutcomeCallable CreateDataSourceFromRedshiftCallable(const Model::CreateDataSourceFromRedshiftRequest& request) const; /** *

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.

See Also:

* AWS * API Reference

* * Queues the request into a thread executor and triggers associated callback when operation has finished. */ virtual void CreateDataSourceFromRedshiftAsync(const Model::CreateDataSourceFromRedshiftRequest& request, const CreateDataSourceFromRedshiftResponseReceivedHandler& handler, const std::shared_ptr& context = nullptr) const; /** *

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.

See Also:

AWS * API Reference

*/ virtual Model::CreateDataSourceFromS3Outcome CreateDataSourceFromS3(const Model::CreateDataSourceFromS3Request& request) const; /** *

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.

See Also:

AWS * API Reference

* * returns a future to the operation so that it can be executed in parallel to other requests. */ virtual Model::CreateDataSourceFromS3OutcomeCallable CreateDataSourceFromS3Callable(const Model::CreateDataSourceFromS3Request& request) const; /** *

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.

See Also:

AWS * API Reference

* * Queues the request into a thread executor and triggers associated callback when operation has finished. */ virtual void CreateDataSourceFromS3Async(const Model::CreateDataSourceFromS3Request& request, const CreateDataSourceFromS3ResponseReceivedHandler& handler, const std::shared_ptr& context = nullptr) const; /** *

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.

See Also:

AWS * API Reference

*/ virtual Model::CreateEvaluationOutcome CreateEvaluation(const Model::CreateEvaluationRequest& request) const; /** *

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.

See Also:

AWS * API Reference

* * returns a future to the operation so that it can be executed in parallel to other requests. */ virtual Model::CreateEvaluationOutcomeCallable CreateEvaluationCallable(const Model::CreateEvaluationRequest& request) const; /** *

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.

See Also:

AWS * API Reference

* * Queues the request into a thread executor and triggers associated callback when operation has finished. */ virtual void CreateEvaluationAsync(const Model::CreateEvaluationRequest& request, const CreateEvaluationResponseReceivedHandler& handler, const std::shared_ptr& context = nullptr) const; /** *

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.

See Also:

* AWS * API Reference

*/ virtual Model::CreateMLModelOutcome CreateMLModel(const Model::CreateMLModelRequest& request) const; /** *

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.

See Also:

* AWS * API Reference

* * returns a future to the operation so that it can be executed in parallel to other requests. */ virtual Model::CreateMLModelOutcomeCallable CreateMLModelCallable(const Model::CreateMLModelRequest& request) const; /** *

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.

See Also:

* AWS * API Reference

* * Queues the request into a thread executor and triggers associated callback when operation has finished. */ virtual void CreateMLModelAsync(const Model::CreateMLModelRequest& request, const CreateMLModelResponseReceivedHandler& handler, const std::shared_ptr& context = nullptr) const; /** *

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.

See Also:

AWS * API Reference

*/ virtual Model::CreateRealtimeEndpointOutcome CreateRealtimeEndpoint(const Model::CreateRealtimeEndpointRequest& request) const; /** *

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.

See Also:

AWS * API Reference

* * returns a future to the operation so that it can be executed in parallel to other requests. */ virtual Model::CreateRealtimeEndpointOutcomeCallable CreateRealtimeEndpointCallable(const Model::CreateRealtimeEndpointRequest& request) const; /** *

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.

See Also:

AWS * API Reference

* * Queues the request into a thread executor and triggers associated callback when operation has finished. */ virtual void CreateRealtimeEndpointAsync(const Model::CreateRealtimeEndpointRequest& request, const CreateRealtimeEndpointResponseReceivedHandler& handler, const std::shared_ptr& context = nullptr) const; /** *

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.

See Also:

AWS * API Reference

*/ virtual Model::DeleteBatchPredictionOutcome DeleteBatchPrediction(const Model::DeleteBatchPredictionRequest& request) const; /** *

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.

See Also:

AWS * API Reference

* * returns a future to the operation so that it can be executed in parallel to other requests. */ virtual Model::DeleteBatchPredictionOutcomeCallable DeleteBatchPredictionCallable(const Model::DeleteBatchPredictionRequest& request) const; /** *

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.

See Also:

AWS * API Reference

* * Queues the request into a thread executor and triggers associated callback when operation has finished. */ virtual void DeleteBatchPredictionAsync(const Model::DeleteBatchPredictionRequest& request, const DeleteBatchPredictionResponseReceivedHandler& handler, const std::shared_ptr& context = nullptr) const; /** *

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.

See * Also:

AWS * API Reference

*/ virtual Model::DeleteDataSourceOutcome DeleteDataSource(const Model::DeleteDataSourceRequest& request) const; /** *

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.

See * Also:

AWS * API Reference

* * returns a future to the operation so that it can be executed in parallel to other requests. */ virtual Model::DeleteDataSourceOutcomeCallable DeleteDataSourceCallable(const Model::DeleteDataSourceRequest& request) const; /** *

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.

See * Also:

AWS * API Reference

* * Queues the request into a thread executor and triggers associated callback when operation has finished. */ virtual void DeleteDataSourceAsync(const Model::DeleteDataSourceRequest& request, const DeleteDataSourceResponseReceivedHandler& handler, const std::shared_ptr& context = nullptr) const; /** *

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.

See Also:

AWS * API Reference

*/ virtual Model::DeleteEvaluationOutcome DeleteEvaluation(const Model::DeleteEvaluationRequest& request) const; /** *

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.

See Also:

AWS * API Reference

* * returns a future to the operation so that it can be executed in parallel to other requests. */ virtual Model::DeleteEvaluationOutcomeCallable DeleteEvaluationCallable(const Model::DeleteEvaluationRequest& request) const; /** *

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.

See Also:

AWS * API Reference

* * Queues the request into a thread executor and triggers associated callback when operation has finished. */ virtual void DeleteEvaluationAsync(const Model::DeleteEvaluationRequest& request, const DeleteEvaluationResponseReceivedHandler& handler, const std::shared_ptr& context = nullptr) const; /** *

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.

See * Also:

AWS * API Reference

*/ virtual Model::DeleteMLModelOutcome DeleteMLModel(const Model::DeleteMLModelRequest& request) const; /** *

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.

See * Also:

AWS * API Reference

* * returns a future to the operation so that it can be executed in parallel to other requests. */ virtual Model::DeleteMLModelOutcomeCallable DeleteMLModelCallable(const Model::DeleteMLModelRequest& request) const; /** *

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.

See * Also:

AWS * API Reference

* * Queues the request into a thread executor and triggers associated callback when operation has finished. */ virtual void DeleteMLModelAsync(const Model::DeleteMLModelRequest& request, const DeleteMLModelResponseReceivedHandler& handler, const std::shared_ptr& context = nullptr) const; /** *

Deletes a real time endpoint of an MLModel.

See * Also:

AWS * API Reference

*/ virtual Model::DeleteRealtimeEndpointOutcome DeleteRealtimeEndpoint(const Model::DeleteRealtimeEndpointRequest& request) const; /** *

Deletes a real time endpoint of an MLModel.

See * Also:

AWS * API Reference

* * returns a future to the operation so that it can be executed in parallel to other requests. */ virtual Model::DeleteRealtimeEndpointOutcomeCallable DeleteRealtimeEndpointCallable(const Model::DeleteRealtimeEndpointRequest& request) const; /** *

Deletes a real time endpoint of an MLModel.

See * Also:

AWS * API Reference

* * Queues the request into a thread executor and triggers associated callback when operation has finished. */ virtual void DeleteRealtimeEndpointAsync(const Model::DeleteRealtimeEndpointRequest& request, const DeleteRealtimeEndpointResponseReceivedHandler& handler, const std::shared_ptr& context = nullptr) const; /** *

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.

See Also:

AWS * API Reference

*/ virtual Model::DeleteTagsOutcome DeleteTags(const Model::DeleteTagsRequest& request) const; /** *

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.

See Also:

AWS * API Reference

* * returns a future to the operation so that it can be executed in parallel to other requests. */ virtual Model::DeleteTagsOutcomeCallable DeleteTagsCallable(const Model::DeleteTagsRequest& request) const; /** *

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.

See Also:

AWS * API Reference

* * Queues the request into a thread executor and triggers associated callback when operation has finished. */ virtual void DeleteTagsAsync(const Model::DeleteTagsRequest& request, const DeleteTagsResponseReceivedHandler& handler, const std::shared_ptr& context = nullptr) const; /** *

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

See Also:

AWS * API Reference

*/ virtual Model::DescribeBatchPredictionsOutcome DescribeBatchPredictions(const Model::DescribeBatchPredictionsRequest& request) const; /** *

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

See Also:

AWS * API Reference

* * returns a future to the operation so that it can be executed in parallel to other requests. */ virtual Model::DescribeBatchPredictionsOutcomeCallable DescribeBatchPredictionsCallable(const Model::DescribeBatchPredictionsRequest& request) const; /** *

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

See Also:

AWS * API Reference

* * Queues the request into a thread executor and triggers associated callback when operation has finished. */ virtual void DescribeBatchPredictionsAsync(const Model::DescribeBatchPredictionsRequest& request, const DescribeBatchPredictionsResponseReceivedHandler& handler, const std::shared_ptr& context = nullptr) const; /** *

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

See Also:

AWS * API Reference

*/ virtual Model::DescribeDataSourcesOutcome DescribeDataSources(const Model::DescribeDataSourcesRequest& request) const; /** *

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

See Also:

AWS * API Reference

* * returns a future to the operation so that it can be executed in parallel to other requests. */ virtual Model::DescribeDataSourcesOutcomeCallable DescribeDataSourcesCallable(const Model::DescribeDataSourcesRequest& request) const; /** *

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

See Also:

AWS * API Reference

* * Queues the request into a thread executor and triggers associated callback when operation has finished. */ virtual void DescribeDataSourcesAsync(const Model::DescribeDataSourcesRequest& request, const DescribeDataSourcesResponseReceivedHandler& handler, const std::shared_ptr& context = nullptr) const; /** *

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

See Also:

AWS * API Reference

*/ virtual Model::DescribeEvaluationsOutcome DescribeEvaluations(const Model::DescribeEvaluationsRequest& request) const; /** *

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

See Also:

AWS * API Reference

* * returns a future to the operation so that it can be executed in parallel to other requests. */ virtual Model::DescribeEvaluationsOutcomeCallable DescribeEvaluationsCallable(const Model::DescribeEvaluationsRequest& request) const; /** *

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

See Also:

AWS * API Reference

* * Queues the request into a thread executor and triggers associated callback when operation has finished. */ virtual void DescribeEvaluationsAsync(const Model::DescribeEvaluationsRequest& request, const DescribeEvaluationsResponseReceivedHandler& handler, const std::shared_ptr& context = nullptr) const; /** *

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

See Also:

AWS * API Reference

*/ virtual Model::DescribeMLModelsOutcome DescribeMLModels(const Model::DescribeMLModelsRequest& request) const; /** *

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

See Also:

AWS * API Reference

* * returns a future to the operation so that it can be executed in parallel to other requests. */ virtual Model::DescribeMLModelsOutcomeCallable DescribeMLModelsCallable(const Model::DescribeMLModelsRequest& request) const; /** *

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

See Also:

AWS * API Reference

* * Queues the request into a thread executor and triggers associated callback when operation has finished. */ virtual void DescribeMLModelsAsync(const Model::DescribeMLModelsRequest& request, const DescribeMLModelsResponseReceivedHandler& handler, const std::shared_ptr& context = nullptr) const; /** *

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

See * Also:

AWS * API Reference

*/ virtual Model::DescribeTagsOutcome DescribeTags(const Model::DescribeTagsRequest& request) const; /** *

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

See * Also:

AWS * API Reference

* * returns a future to the operation so that it can be executed in parallel to other requests. */ virtual Model::DescribeTagsOutcomeCallable DescribeTagsCallable(const Model::DescribeTagsRequest& request) const; /** *

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

See * Also:

AWS * API Reference

* * Queues the request into a thread executor and triggers associated callback when operation has finished. */ virtual void DescribeTagsAsync(const Model::DescribeTagsRequest& request, const DescribeTagsResponseReceivedHandler& handler, const std::shared_ptr& context = nullptr) const; /** *

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

See Also:

AWS * API Reference

*/ virtual Model::GetBatchPredictionOutcome GetBatchPrediction(const Model::GetBatchPredictionRequest& request) const; /** *

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

See Also:

AWS * API Reference

* * returns a future to the operation so that it can be executed in parallel to other requests. */ virtual Model::GetBatchPredictionOutcomeCallable GetBatchPredictionCallable(const Model::GetBatchPredictionRequest& request) const; /** *

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

See Also:

AWS * API Reference

* * Queues the request into a thread executor and triggers associated callback when operation has finished. */ virtual void GetBatchPredictionAsync(const Model::GetBatchPredictionRequest& request, const GetBatchPredictionResponseReceivedHandler& handler, const std::shared_ptr& context = nullptr) const; /** *

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.

See Also:

AWS * API Reference

*/ virtual Model::GetDataSourceOutcome GetDataSource(const Model::GetDataSourceRequest& request) const; /** *

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.

See Also:

AWS * API Reference

* * returns a future to the operation so that it can be executed in parallel to other requests. */ virtual Model::GetDataSourceOutcomeCallable GetDataSourceCallable(const Model::GetDataSourceRequest& request) const; /** *

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.

See Also:

AWS * API Reference

* * Queues the request into a thread executor and triggers associated callback when operation has finished. */ virtual void GetDataSourceAsync(const Model::GetDataSourceRequest& request, const GetDataSourceResponseReceivedHandler& handler, const std::shared_ptr& context = nullptr) const; /** *

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

See Also:

AWS * API Reference

*/ virtual Model::GetEvaluationOutcome GetEvaluation(const Model::GetEvaluationRequest& request) const; /** *

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

See Also:

AWS * API Reference

* * returns a future to the operation so that it can be executed in parallel to other requests. */ virtual Model::GetEvaluationOutcomeCallable GetEvaluationCallable(const Model::GetEvaluationRequest& request) const; /** *

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

See Also:

AWS * API Reference

* * Queues the request into a thread executor and triggers associated callback when operation has finished. */ virtual void GetEvaluationAsync(const Model::GetEvaluationRequest& request, const GetEvaluationResponseReceivedHandler& handler, const std::shared_ptr& context = nullptr) const; /** *

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. *

See Also:

AWS * API Reference

*/ virtual Model::GetMLModelOutcome GetMLModel(const Model::GetMLModelRequest& request) const; /** *

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. *

See Also:

AWS * API Reference

* * returns a future to the operation so that it can be executed in parallel to other requests. */ virtual Model::GetMLModelOutcomeCallable GetMLModelCallable(const Model::GetMLModelRequest& request) const; /** *

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. *

See Also:

AWS * API Reference

* * Queues the request into a thread executor and triggers associated callback when operation has finished. */ virtual void GetMLModelAsync(const Model::GetMLModelRequest& request, const GetMLModelResponseReceivedHandler& handler, const std::shared_ptr& context = nullptr) const; /** *

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.

See Also:

AWS * API Reference

*/ virtual Model::PredictOutcome Predict(const Model::PredictRequest& request) const; /** *

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.

See Also:

AWS * API Reference

* * returns a future to the operation so that it can be executed in parallel to other requests. */ virtual Model::PredictOutcomeCallable PredictCallable(const Model::PredictRequest& request) const; /** *

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.

See Also:

AWS * API Reference

* * Queues the request into a thread executor and triggers associated callback when operation has finished. */ virtual void PredictAsync(const Model::PredictRequest& request, const PredictResponseReceivedHandler& handler, const std::shared_ptr& context = nullptr) const; /** *

Updates the BatchPredictionName of a * BatchPrediction.

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

See Also:

AWS * API Reference

*/ virtual Model::UpdateBatchPredictionOutcome UpdateBatchPrediction(const Model::UpdateBatchPredictionRequest& request) const; /** *

Updates the BatchPredictionName of a * BatchPrediction.

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

See Also:

AWS * API Reference

* * returns a future to the operation so that it can be executed in parallel to other requests. */ virtual Model::UpdateBatchPredictionOutcomeCallable UpdateBatchPredictionCallable(const Model::UpdateBatchPredictionRequest& request) const; /** *

Updates the BatchPredictionName of a * BatchPrediction.

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

See Also:

AWS * API Reference

* * Queues the request into a thread executor and triggers associated callback when operation has finished. */ virtual void UpdateBatchPredictionAsync(const Model::UpdateBatchPredictionRequest& request, const UpdateBatchPredictionResponseReceivedHandler& handler, const std::shared_ptr& context = nullptr) const; /** *

Updates the DataSourceName of a DataSource.

*

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

See Also:

AWS * API Reference

*/ virtual Model::UpdateDataSourceOutcome UpdateDataSource(const Model::UpdateDataSourceRequest& request) const; /** *

Updates the DataSourceName of a DataSource.

*

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

See Also:

AWS * API Reference

* * returns a future to the operation so that it can be executed in parallel to other requests. */ virtual Model::UpdateDataSourceOutcomeCallable UpdateDataSourceCallable(const Model::UpdateDataSourceRequest& request) const; /** *

Updates the DataSourceName of a DataSource.

*

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

See Also:

AWS * API Reference

* * Queues the request into a thread executor and triggers associated callback when operation has finished. */ virtual void UpdateDataSourceAsync(const Model::UpdateDataSourceRequest& request, const UpdateDataSourceResponseReceivedHandler& handler, const std::shared_ptr& context = nullptr) const; /** *

Updates the EvaluationName of an Evaluation.

*

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

See Also:

AWS * API Reference

*/ virtual Model::UpdateEvaluationOutcome UpdateEvaluation(const Model::UpdateEvaluationRequest& request) const; /** *

Updates the EvaluationName of an Evaluation.

*

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

See Also:

AWS * API Reference

* * returns a future to the operation so that it can be executed in parallel to other requests. */ virtual Model::UpdateEvaluationOutcomeCallable UpdateEvaluationCallable(const Model::UpdateEvaluationRequest& request) const; /** *

Updates the EvaluationName of an Evaluation.

*

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

See Also:

AWS * API Reference

* * Queues the request into a thread executor and triggers associated callback when operation has finished. */ virtual void UpdateEvaluationAsync(const Model::UpdateEvaluationRequest& request, const UpdateEvaluationResponseReceivedHandler& handler, const std::shared_ptr& context = nullptr) const; /** *

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:

AWS * API Reference

*/ virtual Model::UpdateMLModelOutcome UpdateMLModel(const Model::UpdateMLModelRequest& request) const; /** *

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:

AWS * API Reference

* * returns a future to the operation so that it can be executed in parallel to other requests. */ virtual Model::UpdateMLModelOutcomeCallable UpdateMLModelCallable(const Model::UpdateMLModelRequest& request) const; /** *

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:

AWS * API Reference

* * Queues the request into a thread executor and triggers associated callback when operation has finished. */ virtual void UpdateMLModelAsync(const Model::UpdateMLModelRequest& request, const UpdateMLModelResponseReceivedHandler& handler, const std::shared_ptr& context = nullptr) const; void OverrideEndpoint(const Aws::String& endpoint); private: void init(const Aws::Client::ClientConfiguration& clientConfiguration); void AddTagsAsyncHelper(const Model::AddTagsRequest& request, const AddTagsResponseReceivedHandler& handler, const std::shared_ptr& context) const; void CreateBatchPredictionAsyncHelper(const Model::CreateBatchPredictionRequest& request, const CreateBatchPredictionResponseReceivedHandler& handler, const std::shared_ptr& context) const; void CreateDataSourceFromRDSAsyncHelper(const Model::CreateDataSourceFromRDSRequest& request, const CreateDataSourceFromRDSResponseReceivedHandler& handler, const std::shared_ptr& context) const; void CreateDataSourceFromRedshiftAsyncHelper(const Model::CreateDataSourceFromRedshiftRequest& request, const CreateDataSourceFromRedshiftResponseReceivedHandler& handler, const std::shared_ptr& context) const; void CreateDataSourceFromS3AsyncHelper(const Model::CreateDataSourceFromS3Request& request, const CreateDataSourceFromS3ResponseReceivedHandler& handler, const std::shared_ptr& context) const; void CreateEvaluationAsyncHelper(const Model::CreateEvaluationRequest& request, const CreateEvaluationResponseReceivedHandler& handler, const std::shared_ptr& context) const; void CreateMLModelAsyncHelper(const Model::CreateMLModelRequest& request, const CreateMLModelResponseReceivedHandler& handler, const std::shared_ptr& context) const; void CreateRealtimeEndpointAsyncHelper(const Model::CreateRealtimeEndpointRequest& request, const CreateRealtimeEndpointResponseReceivedHandler& handler, const std::shared_ptr& context) const; void DeleteBatchPredictionAsyncHelper(const Model::DeleteBatchPredictionRequest& request, const DeleteBatchPredictionResponseReceivedHandler& handler, const std::shared_ptr& context) const; void DeleteDataSourceAsyncHelper(const Model::DeleteDataSourceRequest& request, const DeleteDataSourceResponseReceivedHandler& handler, const std::shared_ptr& context) const; void DeleteEvaluationAsyncHelper(const Model::DeleteEvaluationRequest& request, const DeleteEvaluationResponseReceivedHandler& handler, const std::shared_ptr& context) const; void DeleteMLModelAsyncHelper(const Model::DeleteMLModelRequest& request, const DeleteMLModelResponseReceivedHandler& handler, const std::shared_ptr& context) const; void DeleteRealtimeEndpointAsyncHelper(const Model::DeleteRealtimeEndpointRequest& request, const DeleteRealtimeEndpointResponseReceivedHandler& handler, const std::shared_ptr& context) const; void DeleteTagsAsyncHelper(const Model::DeleteTagsRequest& request, const DeleteTagsResponseReceivedHandler& handler, const std::shared_ptr& context) const; void DescribeBatchPredictionsAsyncHelper(const Model::DescribeBatchPredictionsRequest& request, const DescribeBatchPredictionsResponseReceivedHandler& handler, const std::shared_ptr& context) const; void DescribeDataSourcesAsyncHelper(const Model::DescribeDataSourcesRequest& request, const DescribeDataSourcesResponseReceivedHandler& handler, const std::shared_ptr& context) const; void DescribeEvaluationsAsyncHelper(const Model::DescribeEvaluationsRequest& request, const DescribeEvaluationsResponseReceivedHandler& handler, const std::shared_ptr& context) const; void DescribeMLModelsAsyncHelper(const Model::DescribeMLModelsRequest& request, const DescribeMLModelsResponseReceivedHandler& handler, const std::shared_ptr& context) const; void DescribeTagsAsyncHelper(const Model::DescribeTagsRequest& request, const DescribeTagsResponseReceivedHandler& handler, const std::shared_ptr& context) const; void GetBatchPredictionAsyncHelper(const Model::GetBatchPredictionRequest& request, const GetBatchPredictionResponseReceivedHandler& handler, const std::shared_ptr& context) const; void GetDataSourceAsyncHelper(const Model::GetDataSourceRequest& request, const GetDataSourceResponseReceivedHandler& handler, const std::shared_ptr& context) const; void GetEvaluationAsyncHelper(const Model::GetEvaluationRequest& request, const GetEvaluationResponseReceivedHandler& handler, const std::shared_ptr& context) const; void GetMLModelAsyncHelper(const Model::GetMLModelRequest& request, const GetMLModelResponseReceivedHandler& handler, const std::shared_ptr& context) const; void PredictAsyncHelper(const Model::PredictRequest& request, const PredictResponseReceivedHandler& handler, const std::shared_ptr& context) const; void UpdateBatchPredictionAsyncHelper(const Model::UpdateBatchPredictionRequest& request, const UpdateBatchPredictionResponseReceivedHandler& handler, const std::shared_ptr& context) const; void UpdateDataSourceAsyncHelper(const Model::UpdateDataSourceRequest& request, const UpdateDataSourceResponseReceivedHandler& handler, const std::shared_ptr& context) const; void UpdateEvaluationAsyncHelper(const Model::UpdateEvaluationRequest& request, const UpdateEvaluationResponseReceivedHandler& handler, const std::shared_ptr& context) const; void UpdateMLModelAsyncHelper(const Model::UpdateMLModelRequest& request, const UpdateMLModelResponseReceivedHandler& handler, const std::shared_ptr& context) const; Aws::String m_uri; Aws::String m_configScheme; std::shared_ptr m_executor; }; } // namespace MachineLearning } // namespace Aws