AmazonMachineLearning.java

/*
 * Copyright 2014-2019 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.
 * <p>
 * <b>Note:</b> Do not directly implement this interface, new methods are added to it regularly. Extend from
 * {@link com.amazonaws.services.machinelearning.AbstractAmazonMachineLearning} instead.
 * </p>
 * <p>
 * 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.
     * <p>
     * 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.
     * <p>
     * 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: <a href=
     * "https://docs.aws.amazon.com/sdk-for-java/v1/developer-guide/java-dg-region-selection.html#region-selection-choose-endpoint"
     * > https://docs.aws.amazon.com/sdk-for-java/v1/developer-guide/java-dg-region-selection.html#region-selection-
     * choose-endpoint</a>
     * <p>
     * <b>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.</b>
     *
     * @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.
     * <p>
     * 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.
     * <p>
     * <b>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.</b>
     *
     * @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);

    /**
     * <p>
     * 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, <code>AddTags</code> updates the tag's
     * value.
     * </p>
     * 
     * @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);

    /**
     * <p>
     * Generates predictions for a group of observations. The observations to process exist in one or more data files
     * referenced by a <code>DataSource</code>. This operation creates a new <code>BatchPrediction</code>, and uses an
     * <code>MLModel</code> and the data files referenced by the <code>DataSource</code> as information sources.
     * </p>
     * <p>
     * <code>CreateBatchPrediction</code> is an asynchronous operation. In response to
     * <code>CreateBatchPrediction</code>, Amazon Machine Learning (Amazon ML) immediately returns and sets the
     * <code>BatchPrediction</code> status to <code>PENDING</code>. After the <code>BatchPrediction</code> completes,
     * Amazon ML sets the status to <code>COMPLETED</code>.
     * </p>
     * <p>
     * You can poll for status updates by using the <a>GetBatchPrediction</a> operation and checking the
     * <code>Status</code> parameter of the result. After the <code>COMPLETED</code> status appears, the results are
     * available in the location specified by the <code>OutputUri</code> parameter.
     * </p>
     * 
     * @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);

    /**
     * <p>
     * Creates a <code>DataSource</code> object from an <a href="http://aws.amazon.com/rds/"> Amazon Relational Database
     * Service</a> (Amazon RDS). A <code>DataSource</code> references data that can be used to perform
     * <code>CreateMLModel</code>, <code>CreateEvaluation</code>, or <code>CreateBatchPrediction</code> operations.
     * </p>
     * <p>
     * <code>CreateDataSourceFromRDS</code> is an asynchronous operation. In response to
     * <code>CreateDataSourceFromRDS</code>, Amazon Machine Learning (Amazon ML) immediately returns and sets the
     * <code>DataSource</code> status to <code>PENDING</code>. After the <code>DataSource</code> is created and ready
     * for use, Amazon ML sets the <code>Status</code> parameter to <code>COMPLETED</code>. <code>DataSource</code> in
     * the <code>COMPLETED</code> or <code>PENDING</code> state can be used only to perform
     * <code>&gt;CreateMLModel</code>&gt;, <code>CreateEvaluation</code>, or <code>CreateBatchPrediction</code>
     * operations.
     * </p>
     * <p>
     * If Amazon ML cannot accept the input source, it sets the <code>Status</code> parameter to <code>FAILED</code> and
     * includes an error message in the <code>Message</code> attribute of the <code>GetDataSource</code> operation
     * response.
     * </p>
     * 
     * @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);

    /**
     * <p>
     * Creates a <code>DataSource</code> from a database hosted on an Amazon Redshift cluster. A <code>DataSource</code>
     * references data that can be used to perform either <code>CreateMLModel</code>, <code>CreateEvaluation</code>, or
     * <code>CreateBatchPrediction</code> operations.
     * </p>
     * <p>
     * <code>CreateDataSourceFromRedshift</code> is an asynchronous operation. In response to
     * <code>CreateDataSourceFromRedshift</code>, Amazon Machine Learning (Amazon ML) immediately returns and sets the
     * <code>DataSource</code> status to <code>PENDING</code>. After the <code>DataSource</code> is created and ready
     * for use, Amazon ML sets the <code>Status</code> parameter to <code>COMPLETED</code>. <code>DataSource</code> in
     * <code>COMPLETED</code> or <code>PENDING</code> states can be used to perform only <code>CreateMLModel</code>,
     * <code>CreateEvaluation</code>, or <code>CreateBatchPrediction</code> operations.
     * </p>
     * <p>
     * If Amazon ML can't accept the input source, it sets the <code>Status</code> parameter to <code>FAILED</code> and
     * includes an error message in the <code>Message</code> attribute of the <code>GetDataSource</code> operation
     * response.
     * </p>
     * <p>
     * The observations should be contained in the database hosted on an Amazon Redshift cluster and should be specified
     * by a <code>SelectSqlQuery</code> query. Amazon ML executes an <code>Unload</code> command in Amazon Redshift to
     * transfer the result set of the <code>SelectSqlQuery</code> query to <code>S3StagingLocation</code>.
     * </p>
     * <p>
     * After the <code>DataSource</code> has been created, it's ready for use in evaluations and batch predictions. If
     * you plan to use the <code>DataSource</code> to train an <code>MLModel</code>, the <code>DataSource</code> also
     * requires a recipe. A recipe describes how each input variable will be used in training an <code>MLModel</code>.
     * 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.
     * </p>
     * <?oxy_insert_start author="laurama" timestamp="20160406T153842-0700">
     * <p>
     * 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 <code>GetDataSource</code> for an existing
     * datasource and copy the values to a <code>CreateDataSource</code> call. Change the settings that you want to
     * change and make sure that all required fields have the appropriate values.
     * </p>
     * <?oxy_insert_end>
     * 
     * @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);

    /**
     * <p>
     * Creates a <code>DataSource</code> object. A <code>DataSource</code> references data that can be used to perform
     * <code>CreateMLModel</code>, <code>CreateEvaluation</code>, or <code>CreateBatchPrediction</code> operations.
     * </p>
     * <p>
     * <code>CreateDataSourceFromS3</code> is an asynchronous operation. In response to
     * <code>CreateDataSourceFromS3</code>, Amazon Machine Learning (Amazon ML) immediately returns and sets the
     * <code>DataSource</code> status to <code>PENDING</code>. After the <code>DataSource</code> has been created and is
     * ready for use, Amazon ML sets the <code>Status</code> parameter to <code>COMPLETED</code>.
     * <code>DataSource</code> in the <code>COMPLETED</code> or <code>PENDING</code> state can be used to perform only
     * <code>CreateMLModel</code>, <code>CreateEvaluation</code> or <code>CreateBatchPrediction</code> operations.
     * </p>
     * <p>
     * If Amazon ML can't accept the input source, it sets the <code>Status</code> parameter to <code>FAILED</code> and
     * includes an error message in the <code>Message</code> attribute of the <code>GetDataSource</code> operation
     * response.
     * </p>
     * <p>
     * The observation data used in a <code>DataSource</code> 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 <code>DataSource</code>.
     * </p>
     * <p>
     * After the <code>DataSource</code> has been created, it's ready to use in evaluations and batch predictions. If
     * you plan to use the <code>DataSource</code> to train an <code>MLModel</code>, the <code>DataSource</code> also
     * needs a recipe. A recipe describes how each input variable will be used in training an <code>MLModel</code>. 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.
     * </p>
     * 
     * @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);

    /**
     * <p>
     * Creates a new <code>Evaluation</code> of an <code>MLModel</code>. An <code>MLModel</code> is evaluated on a set
     * of observations associated to a <code>DataSource</code>. Like a <code>DataSource</code> for an
     * <code>MLModel</code>, the <code>DataSource</code> for an <code>Evaluation</code> contains values for the
     * <code>Target Variable</code>. The <code>Evaluation</code> compares the predicted result for each observation to
     * the actual outcome and provides a summary so that you know how effective the <code>MLModel</code> functions on
     * the test data. Evaluation generates a relevant performance metric, such as BinaryAUC, RegressionRMSE or
     * MulticlassAvgFScore based on the corresponding <code>MLModelType</code>: <code>BINARY</code>,
     * <code>REGRESSION</code> or <code>MULTICLASS</code>.
     * </p>
     * <p>
     * <code>CreateEvaluation</code> is an asynchronous operation. In response to <code>CreateEvaluation</code>, Amazon
     * Machine Learning (Amazon ML) immediately returns and sets the evaluation status to <code>PENDING</code>. After
     * the <code>Evaluation</code> is created and ready for use, Amazon ML sets the status to <code>COMPLETED</code>.
     * </p>
     * <p>
     * You can use the <code>GetEvaluation</code> operation to check progress of the evaluation during the creation
     * operation.
     * </p>
     * 
     * @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);

    /**
     * <p>
     * Creates a new <code>MLModel</code> using the <code>DataSource</code> and the recipe as information sources.
     * </p>
     * <p>
     * An <code>MLModel</code> is nearly immutable. Users can update only the <code>MLModelName</code> and the
     * <code>ScoreThreshold</code> in an <code>MLModel</code> without creating a new <code>MLModel</code>.
     * </p>
     * <p>
     * <code>CreateMLModel</code> is an asynchronous operation. In response to <code>CreateMLModel</code>, Amazon
     * Machine Learning (Amazon ML) immediately returns and sets the <code>MLModel</code> status to <code>PENDING</code>
     * . After the <code>MLModel</code> has been created and ready is for use, Amazon ML sets the status to
     * <code>COMPLETED</code>.
     * </p>
     * <p>
     * You can use the <code>GetMLModel</code> operation to check the progress of the <code>MLModel</code> during the
     * creation operation.
     * </p>
     * <p>
     * <code>CreateMLModel</code> requires a <code>DataSource</code> with computed statistics, which can be created by
     * setting <code>ComputeStatistics</code> to <code>true</code> in <code>CreateDataSourceFromRDS</code>,
     * <code>CreateDataSourceFromS3</code>, or <code>CreateDataSourceFromRedshift</code> operations.
     * </p>
     * 
     * @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);

    /**
     * <p>
     * Creates a real-time endpoint for the <code>MLModel</code>. The endpoint contains the URI of the
     * <code>MLModel</code>; that is, the location to send real-time prediction requests for the specified
     * <code>MLModel</code>.
     * </p>
     * 
     * @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);

    /**
     * <p>
     * Assigns the DELETED status to a <code>BatchPrediction</code>, rendering it unusable.
     * </p>
     * <p>
     * After using the <code>DeleteBatchPrediction</code> operation, you can use the <a>GetBatchPrediction</a> operation
     * to verify that the status of the <code>BatchPrediction</code> changed to DELETED.
     * </p>
     * <p>
     * <b>Caution:</b> The result of the <code>DeleteBatchPrediction</code> operation is irreversible.
     * </p>
     * 
     * @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);

    /**
     * <p>
     * Assigns the DELETED status to a <code>DataSource</code>, rendering it unusable.
     * </p>
     * <p>
     * After using the <code>DeleteDataSource</code> operation, you can use the <a>GetDataSource</a> operation to verify
     * that the status of the <code>DataSource</code> changed to DELETED.
     * </p>
     * <p>
     * <b>Caution:</b> The results of the <code>DeleteDataSource</code> operation are irreversible.
     * </p>
     * 
     * @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);

    /**
     * <p>
     * Assigns the <code>DELETED</code> status to an <code>Evaluation</code>, rendering it unusable.
     * </p>
     * <p>
     * After invoking the <code>DeleteEvaluation</code> operation, you can use the <code>GetEvaluation</code> operation
     * to verify that the status of the <code>Evaluation</code> changed to <code>DELETED</code>.
     * </p>
     * <caution><title>Caution</title>
     * <p>
     * The results of the <code>DeleteEvaluation</code> operation are irreversible.
     * </p>
     * </caution>
     * 
     * @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);

    /**
     * <p>
     * Assigns the <code>DELETED</code> status to an <code>MLModel</code>, rendering it unusable.
     * </p>
     * <p>
     * After using the <code>DeleteMLModel</code> operation, you can use the <code>GetMLModel</code> operation to verify
     * that the status of the <code>MLModel</code> changed to DELETED.
     * </p>
     * <p>
     * <b>Caution:</b> The result of the <code>DeleteMLModel</code> operation is irreversible.
     * </p>
     * 
     * @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);

    /**
     * <p>
     * Deletes a real time endpoint of an <code>MLModel</code>.
     * </p>
     * 
     * @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);

    /**
     * <p>
     * Deletes the specified tags associated with an ML object. After this operation is complete, you can't recover
     * deleted tags.
     * </p>
     * <p>
     * If you specify a tag that doesn't exist, Amazon ML ignores it.
     * </p>
     * 
     * @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);

    /**
     * <p>
     * Returns a list of <code>BatchPrediction</code> operations that match the search criteria in the request.
     * </p>
     * 
     * @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();

    /**
     * <p>
     * Returns a list of <code>DataSource</code> that match the search criteria in the request.
     * </p>
     * 
     * @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();

    /**
     * <p>
     * Returns a list of <code>DescribeEvaluations</code> that match the search criteria in the request.
     * </p>
     * 
     * @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();

    /**
     * <p>
     * Returns a list of <code>MLModel</code> that match the search criteria in the request.
     * </p>
     * 
     * @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();

    /**
     * <p>
     * Describes one or more of the tags for your Amazon ML object.
     * </p>
     * 
     * @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);

    /**
     * <p>
     * Returns a <code>BatchPrediction</code> that includes detailed metadata, status, and data file information for a
     * <code>Batch Prediction</code> request.
     * </p>
     * 
     * @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);

    /**
     * <p>
     * Returns a <code>DataSource</code> that includes metadata and data file information, as well as the current status
     * of the <code>DataSource</code>.
     * </p>
     * <p>
     * <code>GetDataSource</code> 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.
     * </p>
     * 
     * @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);

    /**
     * <p>
     * Returns an <code>Evaluation</code> that includes metadata as well as the current status of the
     * <code>Evaluation</code>.
     * </p>
     * 
     * @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);

    /**
     * <p>
     * Returns an <code>MLModel</code> that includes detailed metadata, data source information, and the current status
     * of the <code>MLModel</code>.
     * </p>
     * <p>
     * <code>GetMLModel</code> provides results in normal or verbose format.
     * </p>
     * 
     * @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);

    /**
     * <p>
     * Generates a prediction for the observation using the specified <code>ML Model</code>.
     * </p>
     * <note><title>Note</title>
     * <p>
     * Not all response parameters will be populated. Whether a response parameter is populated depends on the type of
     * model requested.
     * </p>
     * </note>
     * 
     * @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 <code>DataSource</code>.
     * @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 <code>MLModel</code>.
     * @sample AmazonMachineLearning.Predict
     */
    PredictResult predict(PredictRequest predictRequest);

    /**
     * <p>
     * Updates the <code>BatchPredictionName</code> of a <code>BatchPrediction</code>.
     * </p>
     * <p>
     * You can use the <code>GetBatchPrediction</code> operation to view the contents of the updated data element.
     * </p>
     * 
     * @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);

    /**
     * <p>
     * Updates the <code>DataSourceName</code> of a <code>DataSource</code>.
     * </p>
     * <p>
     * You can use the <code>GetDataSource</code> operation to view the contents of the updated data element.
     * </p>
     * 
     * @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);

    /**
     * <p>
     * Updates the <code>EvaluationName</code> of an <code>Evaluation</code>.
     * </p>
     * <p>
     * You can use the <code>GetEvaluation</code> operation to view the contents of the updated data element.
     * </p>
     * 
     * @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);

    /**
     * <p>
     * Updates the <code>MLModelName</code> and the <code>ScoreThreshold</code> of an <code>MLModel</code>.
     * </p>
     * <p>
     * You can use the <code>GetMLModel</code> operation to view the contents of the updated data element.
     * </p>
     * 
     * @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.
     * <p>
     * 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();

}