/* * Copyright 2018-2023 Amazon.com, Inc. or its affiliates. All Rights Reserved. * * Licensed under the Apache License, Version 2.0 (the "License"). You may not use this file except in compliance with * the License. A copy of the License is located at * * http://aws.amazon.com/apache2.0 * * or in the "license" file accompanying this file. This file is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR * CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions * and limitations under the License. */ package com.amazonaws.services.machinelearning; import javax.annotation.Generated; import com.amazonaws.*; import com.amazonaws.regions.*; import com.amazonaws.services.machinelearning.model.*; import com.amazonaws.services.machinelearning.waiters.AmazonMachineLearningWaiters; /** * Interface for accessing Amazon Machine Learning. *

* Note: Do not directly implement this interface, new methods are added to it regularly. Extend from * {@link com.amazonaws.services.machinelearning.AbstractAmazonMachineLearning} instead. *

*

* Definition of the public APIs exposed by Amazon Machine Learning */ @Generated("com.amazonaws:aws-java-sdk-code-generator") public interface AmazonMachineLearning { /** * The region metadata service name for computing region endpoints. You can use this value to retrieve metadata * (such as supported regions) of the service. * * @see RegionUtils#getRegionsForService(String) */ String ENDPOINT_PREFIX = "machinelearning"; /** * Overrides the default endpoint for this client ("https://machinelearning.us-east-1.amazonaws.com"). Callers can * use this method to control which AWS region they want to work with. *

* Callers can pass in just the endpoint (ex: "machinelearning.us-east-1.amazonaws.com") or a full URL, including * the protocol (ex: "https://machinelearning.us-east-1.amazonaws.com"). If the protocol is not specified here, the * default protocol from this client's {@link ClientConfiguration} will be used, which by default is HTTPS. *

* For more information on using AWS regions with the AWS SDK for Java, and a complete list of all available * endpoints for all AWS services, see: https://docs.aws.amazon.com/sdk-for-java/v1/developer-guide/java-dg-region-selection.html#region-selection- * choose-endpoint *

* This method is not threadsafe. An endpoint should be configured when the client is created and before any * service requests are made. Changing it afterwards creates inevitable race conditions for any service requests in * transit or retrying. * * @param endpoint * The endpoint (ex: "machinelearning.us-east-1.amazonaws.com") or a full URL, including the protocol (ex: * "https://machinelearning.us-east-1.amazonaws.com") of the region specific AWS endpoint this client will * communicate with. * @deprecated use {@link AwsClientBuilder#setEndpointConfiguration(AwsClientBuilder.EndpointConfiguration)} for * example: * {@code builder.setEndpointConfiguration(new EndpointConfiguration(endpoint, signingRegion));} */ @Deprecated void setEndpoint(String endpoint); /** * An alternative to {@link AmazonMachineLearning#setEndpoint(String)}, sets the regional endpoint for this client's * service calls. Callers can use this method to control which AWS region they want to work with. *

* By default, all service endpoints in all regions use the https protocol. To use http instead, specify it in the * {@link ClientConfiguration} supplied at construction. *

* This method is not threadsafe. A region should be configured when the client is created and before any service * requests are made. Changing it afterwards creates inevitable race conditions for any service requests in transit * or retrying. * * @param region * The region this client will communicate with. See {@link Region#getRegion(com.amazonaws.regions.Regions)} * for accessing a given region. Must not be null and must be a region where the service is available. * * @see Region#getRegion(com.amazonaws.regions.Regions) * @see Region#createClient(Class, com.amazonaws.auth.AWSCredentialsProvider, ClientConfiguration) * @see Region#isServiceSupported(String) * @deprecated use {@link AwsClientBuilder#setRegion(String)} */ @Deprecated void setRegion(Region region); /** *

* 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. *

* * @param addTagsRequest * @return Result of the AddTags operation returned by the service. * @throws InvalidInputException * An error on the client occurred. Typically, the cause is an invalid input value. * @throws InvalidTagException * @throws TagLimitExceededException * @throws ResourceNotFoundException * A specified resource cannot be located. * @throws InternalServerException * An error on the server occurred when trying to process a request. * @sample AmazonMachineLearning.AddTags */ AddTagsResult addTags(AddTagsRequest addTagsRequest); /** *

* 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. *

* * @param createBatchPredictionRequest * @return Result of the CreateBatchPrediction operation returned by the service. * @throws InvalidInputException * An error on the client occurred. Typically, the cause is an invalid input value. * @throws InternalServerException * An error on the server occurred when trying to process a request. * @throws IdempotentParameterMismatchException * A second request to use or change an object was not allowed. This can result from retrying a request * using a parameter that was not present in the original request. * @sample AmazonMachineLearning.CreateBatchPrediction */ CreateBatchPredictionResult createBatchPrediction(CreateBatchPredictionRequest createBatchPredictionRequest); /** *

* 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. *

* * @param createDataSourceFromRDSRequest * @return Result of the CreateDataSourceFromRDS operation returned by the service. * @throws InvalidInputException * An error on the client occurred. Typically, the cause is an invalid input value. * @throws InternalServerException * An error on the server occurred when trying to process a request. * @throws IdempotentParameterMismatchException * A second request to use or change an object was not allowed. This can result from retrying a request * using a parameter that was not present in the original request. * @sample AmazonMachineLearning.CreateDataSourceFromRDS */ CreateDataSourceFromRDSResult createDataSourceFromRDS(CreateDataSourceFromRDSRequest createDataSourceFromRDSRequest); /** *

* 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. *

* * @param createDataSourceFromRedshiftRequest * @return Result of the CreateDataSourceFromRedshift operation returned by the service. * @throws InvalidInputException * An error on the client occurred. Typically, the cause is an invalid input value. * @throws InternalServerException * An error on the server occurred when trying to process a request. * @throws IdempotentParameterMismatchException * A second request to use or change an object was not allowed. This can result from retrying a request * using a parameter that was not present in the original request. * @sample AmazonMachineLearning.CreateDataSourceFromRedshift */ CreateDataSourceFromRedshiftResult createDataSourceFromRedshift(CreateDataSourceFromRedshiftRequest createDataSourceFromRedshiftRequest); /** *

* 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. *

* * @param createDataSourceFromS3Request * @return Result of the CreateDataSourceFromS3 operation returned by the service. * @throws InvalidInputException * An error on the client occurred. Typically, the cause is an invalid input value. * @throws InternalServerException * An error on the server occurred when trying to process a request. * @throws IdempotentParameterMismatchException * A second request to use or change an object was not allowed. This can result from retrying a request * using a parameter that was not present in the original request. * @sample AmazonMachineLearning.CreateDataSourceFromS3 */ CreateDataSourceFromS3Result createDataSourceFromS3(CreateDataSourceFromS3Request createDataSourceFromS3Request); /** *

* 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. *

* * @param createEvaluationRequest * @return Result of the CreateEvaluation operation returned by the service. * @throws InvalidInputException * An error on the client occurred. Typically, the cause is an invalid input value. * @throws InternalServerException * An error on the server occurred when trying to process a request. * @throws IdempotentParameterMismatchException * A second request to use or change an object was not allowed. This can result from retrying a request * using a parameter that was not present in the original request. * @sample AmazonMachineLearning.CreateEvaluation */ CreateEvaluationResult createEvaluation(CreateEvaluationRequest createEvaluationRequest); /** *

* 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. *

* * @param createMLModelRequest * @return Result of the CreateMLModel operation returned by the service. * @throws InvalidInputException * An error on the client occurred. Typically, the cause is an invalid input value. * @throws InternalServerException * An error on the server occurred when trying to process a request. * @throws IdempotentParameterMismatchException * A second request to use or change an object was not allowed. This can result from retrying a request * using a parameter that was not present in the original request. * @sample AmazonMachineLearning.CreateMLModel */ CreateMLModelResult createMLModel(CreateMLModelRequest createMLModelRequest); /** *

* 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. *

* * @param createRealtimeEndpointRequest * @return Result of the CreateRealtimeEndpoint operation returned by the service. * @throws InvalidInputException * An error on the client occurred. Typically, the cause is an invalid input value. * @throws ResourceNotFoundException * A specified resource cannot be located. * @throws InternalServerException * An error on the server occurred when trying to process a request. * @sample AmazonMachineLearning.CreateRealtimeEndpoint */ CreateRealtimeEndpointResult createRealtimeEndpoint(CreateRealtimeEndpointRequest createRealtimeEndpointRequest); /** *

* 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. *

* * @param deleteBatchPredictionRequest * @return Result of the DeleteBatchPrediction operation returned by the service. * @throws InvalidInputException * An error on the client occurred. Typically, the cause is an invalid input value. * @throws ResourceNotFoundException * A specified resource cannot be located. * @throws InternalServerException * An error on the server occurred when trying to process a request. * @sample AmazonMachineLearning.DeleteBatchPrediction */ DeleteBatchPredictionResult deleteBatchPrediction(DeleteBatchPredictionRequest deleteBatchPredictionRequest); /** *

* 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. *

* * @param deleteDataSourceRequest * @return Result of the DeleteDataSource operation returned by the service. * @throws InvalidInputException * An error on the client occurred. Typically, the cause is an invalid input value. * @throws ResourceNotFoundException * A specified resource cannot be located. * @throws InternalServerException * An error on the server occurred when trying to process a request. * @sample AmazonMachineLearning.DeleteDataSource */ DeleteDataSourceResult deleteDataSource(DeleteDataSourceRequest deleteDataSourceRequest); /** *

* 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. *

* * @param deleteEvaluationRequest * @return Result of the DeleteEvaluation operation returned by the service. * @throws InvalidInputException * An error on the client occurred. Typically, the cause is an invalid input value. * @throws ResourceNotFoundException * A specified resource cannot be located. * @throws InternalServerException * An error on the server occurred when trying to process a request. * @sample AmazonMachineLearning.DeleteEvaluation */ DeleteEvaluationResult deleteEvaluation(DeleteEvaluationRequest deleteEvaluationRequest); /** *

* 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. *

* * @param deleteMLModelRequest * @return Result of the DeleteMLModel operation returned by the service. * @throws InvalidInputException * An error on the client occurred. Typically, the cause is an invalid input value. * @throws ResourceNotFoundException * A specified resource cannot be located. * @throws InternalServerException * An error on the server occurred when trying to process a request. * @sample AmazonMachineLearning.DeleteMLModel */ DeleteMLModelResult deleteMLModel(DeleteMLModelRequest deleteMLModelRequest); /** *

* Deletes a real time endpoint of an MLModel. *

* * @param deleteRealtimeEndpointRequest * @return Result of the DeleteRealtimeEndpoint operation returned by the service. * @throws InvalidInputException * An error on the client occurred. Typically, the cause is an invalid input value. * @throws ResourceNotFoundException * A specified resource cannot be located. * @throws InternalServerException * An error on the server occurred when trying to process a request. * @sample AmazonMachineLearning.DeleteRealtimeEndpoint */ DeleteRealtimeEndpointResult deleteRealtimeEndpoint(DeleteRealtimeEndpointRequest deleteRealtimeEndpointRequest); /** *

* 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. *

* * @param deleteTagsRequest * @return Result of the DeleteTags operation returned by the service. * @throws InvalidInputException * An error on the client occurred. Typically, the cause is an invalid input value. * @throws InvalidTagException * @throws ResourceNotFoundException * A specified resource cannot be located. * @throws InternalServerException * An error on the server occurred when trying to process a request. * @sample AmazonMachineLearning.DeleteTags */ DeleteTagsResult deleteTags(DeleteTagsRequest deleteTagsRequest); /** *

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

* * @param describeBatchPredictionsRequest * @return Result of the DescribeBatchPredictions operation returned by the service. * @throws InvalidInputException * An error on the client occurred. Typically, the cause is an invalid input value. * @throws InternalServerException * An error on the server occurred when trying to process a request. * @sample AmazonMachineLearning.DescribeBatchPredictions */ DescribeBatchPredictionsResult describeBatchPredictions(DescribeBatchPredictionsRequest describeBatchPredictionsRequest); /** * Simplified method form for invoking the DescribeBatchPredictions operation. * * @see #describeBatchPredictions(DescribeBatchPredictionsRequest) */ DescribeBatchPredictionsResult describeBatchPredictions(); /** *

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

* * @param describeDataSourcesRequest * @return Result of the DescribeDataSources operation returned by the service. * @throws InvalidInputException * An error on the client occurred. Typically, the cause is an invalid input value. * @throws InternalServerException * An error on the server occurred when trying to process a request. * @sample AmazonMachineLearning.DescribeDataSources */ DescribeDataSourcesResult describeDataSources(DescribeDataSourcesRequest describeDataSourcesRequest); /** * Simplified method form for invoking the DescribeDataSources operation. * * @see #describeDataSources(DescribeDataSourcesRequest) */ DescribeDataSourcesResult describeDataSources(); /** *

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

* * @param describeEvaluationsRequest * @return Result of the DescribeEvaluations operation returned by the service. * @throws InvalidInputException * An error on the client occurred. Typically, the cause is an invalid input value. * @throws InternalServerException * An error on the server occurred when trying to process a request. * @sample AmazonMachineLearning.DescribeEvaluations */ DescribeEvaluationsResult describeEvaluations(DescribeEvaluationsRequest describeEvaluationsRequest); /** * Simplified method form for invoking the DescribeEvaluations operation. * * @see #describeEvaluations(DescribeEvaluationsRequest) */ DescribeEvaluationsResult describeEvaluations(); /** *

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

* * @param describeMLModelsRequest * @return Result of the DescribeMLModels operation returned by the service. * @throws InvalidInputException * An error on the client occurred. Typically, the cause is an invalid input value. * @throws InternalServerException * An error on the server occurred when trying to process a request. * @sample AmazonMachineLearning.DescribeMLModels */ DescribeMLModelsResult describeMLModels(DescribeMLModelsRequest describeMLModelsRequest); /** * Simplified method form for invoking the DescribeMLModels operation. * * @see #describeMLModels(DescribeMLModelsRequest) */ DescribeMLModelsResult describeMLModels(); /** *

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

* * @param describeTagsRequest * @return Result of the DescribeTags operation returned by the service. * @throws InvalidInputException * An error on the client occurred. Typically, the cause is an invalid input value. * @throws ResourceNotFoundException * A specified resource cannot be located. * @throws InternalServerException * An error on the server occurred when trying to process a request. * @sample AmazonMachineLearning.DescribeTags */ DescribeTagsResult describeTags(DescribeTagsRequest describeTagsRequest); /** *

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

* * @param getBatchPredictionRequest * @return Result of the GetBatchPrediction operation returned by the service. * @throws InvalidInputException * An error on the client occurred. Typically, the cause is an invalid input value. * @throws ResourceNotFoundException * A specified resource cannot be located. * @throws InternalServerException * An error on the server occurred when trying to process a request. * @sample AmazonMachineLearning.GetBatchPrediction */ GetBatchPredictionResult getBatchPrediction(GetBatchPredictionRequest getBatchPredictionRequest); /** *

* 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. *

* * @param getDataSourceRequest * @return Result of the GetDataSource operation returned by the service. * @throws InvalidInputException * An error on the client occurred. Typically, the cause is an invalid input value. * @throws ResourceNotFoundException * A specified resource cannot be located. * @throws InternalServerException * An error on the server occurred when trying to process a request. * @sample AmazonMachineLearning.GetDataSource */ GetDataSourceResult getDataSource(GetDataSourceRequest getDataSourceRequest); /** *

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

* * @param getEvaluationRequest * @return Result of the GetEvaluation operation returned by the service. * @throws InvalidInputException * An error on the client occurred. Typically, the cause is an invalid input value. * @throws ResourceNotFoundException * A specified resource cannot be located. * @throws InternalServerException * An error on the server occurred when trying to process a request. * @sample AmazonMachineLearning.GetEvaluation */ GetEvaluationResult getEvaluation(GetEvaluationRequest getEvaluationRequest); /** *

* 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. *

* * @param getMLModelRequest * @return Result of the GetMLModel operation returned by the service. * @throws InvalidInputException * An error on the client occurred. Typically, the cause is an invalid input value. * @throws ResourceNotFoundException * A specified resource cannot be located. * @throws InternalServerException * An error on the server occurred when trying to process a request. * @sample AmazonMachineLearning.GetMLModel */ GetMLModelResult getMLModel(GetMLModelRequest getMLModelRequest); /** *

* 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. *

* * @param predictRequest * @return Result of the Predict operation returned by the service. * @throws InvalidInputException * An error on the client occurred. Typically, the cause is an invalid input value. * @throws ResourceNotFoundException * A specified resource cannot be located. * @throws LimitExceededException * The subscriber exceeded the maximum number of operations. This exception can occur when listing objects * such as DataSource. * @throws InternalServerException * An error on the server occurred when trying to process a request. * @throws PredictorNotMountedException * The exception is thrown when a predict request is made to an unmounted MLModel. * @sample AmazonMachineLearning.Predict */ PredictResult predict(PredictRequest predictRequest); /** *

* Updates the BatchPredictionName of a BatchPrediction. *

*

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

* * @param updateBatchPredictionRequest * @return Result of the UpdateBatchPrediction operation returned by the service. * @throws InvalidInputException * An error on the client occurred. Typically, the cause is an invalid input value. * @throws ResourceNotFoundException * A specified resource cannot be located. * @throws InternalServerException * An error on the server occurred when trying to process a request. * @sample AmazonMachineLearning.UpdateBatchPrediction */ UpdateBatchPredictionResult updateBatchPrediction(UpdateBatchPredictionRequest updateBatchPredictionRequest); /** *

* Updates the DataSourceName of a DataSource. *

*

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

* * @param updateDataSourceRequest * @return Result of the UpdateDataSource operation returned by the service. * @throws InvalidInputException * An error on the client occurred. Typically, the cause is an invalid input value. * @throws ResourceNotFoundException * A specified resource cannot be located. * @throws InternalServerException * An error on the server occurred when trying to process a request. * @sample AmazonMachineLearning.UpdateDataSource */ UpdateDataSourceResult updateDataSource(UpdateDataSourceRequest updateDataSourceRequest); /** *

* Updates the EvaluationName of an Evaluation. *

*

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

* * @param updateEvaluationRequest * @return Result of the UpdateEvaluation operation returned by the service. * @throws InvalidInputException * An error on the client occurred. Typically, the cause is an invalid input value. * @throws ResourceNotFoundException * A specified resource cannot be located. * @throws InternalServerException * An error on the server occurred when trying to process a request. * @sample AmazonMachineLearning.UpdateEvaluation */ UpdateEvaluationResult updateEvaluation(UpdateEvaluationRequest updateEvaluationRequest); /** *

* Updates the MLModelName and the ScoreThreshold of an MLModel. *

*

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

* * @param updateMLModelRequest * @return Result of the UpdateMLModel operation returned by the service. * @throws InvalidInputException * An error on the client occurred. Typically, the cause is an invalid input value. * @throws ResourceNotFoundException * A specified resource cannot be located. * @throws InternalServerException * An error on the server occurred when trying to process a request. * @sample AmazonMachineLearning.UpdateMLModel */ UpdateMLModelResult updateMLModel(UpdateMLModelRequest updateMLModelRequest); /** * Shuts down this client object, releasing any resources that might be held open. This is an optional method, and * callers are not expected to call it, but can if they want to explicitly release any open resources. Once a client * has been shutdown, it should not be used to make any more requests. */ void shutdown(); /** * Returns additional metadata for a previously executed successful request, typically used for debugging issues * where a service isn't acting as expected. This data isn't considered part of the result data returned by an * operation, so it's available through this separate, diagnostic interface. *

* Response metadata is only cached for a limited period of time, so if you need to access this extra diagnostic * information for an executed request, you should use this method to retrieve it as soon as possible after * executing a request. * * @param request * The originally executed request. * * @return The response metadata for the specified request, or null if none is available. */ ResponseMetadata getCachedResponseMetadata(AmazonWebServiceRequest request); AmazonMachineLearningWaiters waiters(); }