// // Copyright 2010-2018 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. // #import #import #import "AWSMachineLearningModel.h" #import "AWSMachineLearningResources.h" NS_ASSUME_NONNULL_BEGIN //! SDK version for AWSMachineLearning FOUNDATION_EXPORT NSString *const AWSMachineLearningSDKVersion; /** Definition of the public APIs exposed by Amazon Machine Learning */ @interface AWSMachineLearning : AWSService /** The service configuration used to instantiate this service client. @warning Once the client is instantiated, do not modify the configuration object. It may cause unspecified behaviors. */ @property (nonatomic, strong, readonly) AWSServiceConfiguration *configuration; /** Returns the singleton service client. If the singleton object does not exist, the SDK instantiates the default service client with `defaultServiceConfiguration` from `[AWSServiceManager defaultServiceManager]`. The reference to this object is maintained by the SDK, and you do not need to retain it manually. For example, set the default service configuration in `- application:didFinishLaunchingWithOptions:` *Swift* func application(_ application: UIApplication, didFinishLaunchingWithOptions launchOptions: [UIApplicationLaunchOptionsKey: Any]?) -> Bool { let credentialProvider = AWSCognitoCredentialsProvider(regionType: .USEast1, identityPoolId: "YourIdentityPoolId") let configuration = AWSServiceConfiguration(region: .USEast1, credentialsProvider: credentialProvider) AWSServiceManager.default().defaultServiceConfiguration = configuration return true } *Objective-C* - (BOOL)application:(UIApplication *)application didFinishLaunchingWithOptions:(NSDictionary *)launchOptions { AWSCognitoCredentialsProvider *credentialsProvider = [[AWSCognitoCredentialsProvider alloc] initWithRegionType:AWSRegionUSEast1 identityPoolId:@"YourIdentityPoolId"]; AWSServiceConfiguration *configuration = [[AWSServiceConfiguration alloc] initWithRegion:AWSRegionUSEast1 credentialsProvider:credentialsProvider]; [AWSServiceManager defaultServiceManager].defaultServiceConfiguration = configuration; return YES; } Then call the following to get the default service client: *Swift* let MachineLearning = AWSMachineLearning.default() *Objective-C* AWSMachineLearning *MachineLearning = [AWSMachineLearning defaultMachineLearning]; @return The default service client. */ + (instancetype)defaultMachineLearning; /** Creates a service client with the given service configuration and registers it for the key. For example, set the default service configuration in `- application:didFinishLaunchingWithOptions:` *Swift* func application(_ application: UIApplication, didFinishLaunchingWithOptions launchOptions: [UIApplicationLaunchOptionsKey: Any]?) -> Bool { let credentialProvider = AWSCognitoCredentialsProvider(regionType: .USEast1, identityPoolId: "YourIdentityPoolId") let configuration = AWSServiceConfiguration(region: .USWest2, credentialsProvider: credentialProvider) AWSMachineLearning.register(with: configuration!, forKey: "USWest2MachineLearning") return true } *Objective-C* - (BOOL)application:(UIApplication *)application didFinishLaunchingWithOptions:(NSDictionary *)launchOptions { AWSCognitoCredentialsProvider *credentialsProvider = [[AWSCognitoCredentialsProvider alloc] initWithRegionType:AWSRegionUSEast1 identityPoolId:@"YourIdentityPoolId"]; AWSServiceConfiguration *configuration = [[AWSServiceConfiguration alloc] initWithRegion:AWSRegionUSWest2 credentialsProvider:credentialsProvider]; [AWSMachineLearning registerMachineLearningWithConfiguration:configuration forKey:@"USWest2MachineLearning"]; return YES; } Then call the following to get the service client: *Swift* let MachineLearning = AWSMachineLearning(forKey: "USWest2MachineLearning") *Objective-C* AWSMachineLearning *MachineLearning = [AWSMachineLearning MachineLearningForKey:@"USWest2MachineLearning"]; @warning After calling this method, do not modify the configuration object. It may cause unspecified behaviors. @param configuration A service configuration object. @param key A string to identify the service client. */ + (void)registerMachineLearningWithConfiguration:(AWSServiceConfiguration *)configuration forKey:(NSString *)key; /** Retrieves the service client associated with the key. You need to call `+ registerMachineLearningWithConfiguration:forKey:` before invoking this method. For example, set the default service configuration in `- application:didFinishLaunchingWithOptions:` *Swift* func application(_ application: UIApplication, didFinishLaunchingWithOptions launchOptions: [UIApplicationLaunchOptionsKey: Any]?) -> Bool { let credentialProvider = AWSCognitoCredentialsProvider(regionType: .USEast1, identityPoolId: "YourIdentityPoolId") let configuration = AWSServiceConfiguration(region: .USWest2, credentialsProvider: credentialProvider) AWSMachineLearning.register(with: configuration!, forKey: "USWest2MachineLearning") return true } *Objective-C* - (BOOL)application:(UIApplication *)application didFinishLaunchingWithOptions:(NSDictionary *)launchOptions { AWSCognitoCredentialsProvider *credentialsProvider = [[AWSCognitoCredentialsProvider alloc] initWithRegionType:AWSRegionUSEast1 identityPoolId:@"YourIdentityPoolId"]; AWSServiceConfiguration *configuration = [[AWSServiceConfiguration alloc] initWithRegion:AWSRegionUSWest2 credentialsProvider:credentialsProvider]; [AWSMachineLearning registerMachineLearningWithConfiguration:configuration forKey:@"USWest2MachineLearning"]; return YES; } Then call the following to get the service client: *Swift* let MachineLearning = AWSMachineLearning(forKey: "USWest2MachineLearning") *Objective-C* AWSMachineLearning *MachineLearning = [AWSMachineLearning MachineLearningForKey:@"USWest2MachineLearning"]; @param key A string to identify the service client. @return An instance of the service client. */ + (instancetype)MachineLearningForKey:(NSString *)key; /** Removes the service client associated with the key and release it. @warning Before calling this method, make sure no method is running on this client. @param key A string to identify the service client. */ + (void)removeMachineLearningForKey:(NSString *)key; /**

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 request A container for the necessary parameters to execute the AddTags service method. @return An instance of `AWSTask`. On successful execution, `task.result` will contain an instance of `AWSMachineLearningAddTagsOutput`. On failed execution, `task.error` may contain an `NSError` with `AWSMachineLearningErrorDomain` domain and the following error code: `AWSMachineLearningErrorInvalidInput`, `AWSMachineLearningErrorInvalidTag`, `AWSMachineLearningErrorTagLimitExceeded`, `AWSMachineLearningErrorResourceNotFound`, `AWSMachineLearningErrorInternalServer`. @see AWSMachineLearningAddTagsInput @see AWSMachineLearningAddTagsOutput */ - (AWSTask *)addTags:(AWSMachineLearningAddTagsInput *)request; /**

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 request A container for the necessary parameters to execute the AddTags service method. @param completionHandler The completion handler to call when the load request is complete. `response` - A response object, or `nil` if the request failed. `error` - An error object that indicates why the request failed, or `nil` if the request was successful. On failed execution, `error` may contain an `NSError` with `AWSMachineLearningErrorDomain` domain and the following error code: `AWSMachineLearningErrorInvalidInput`, `AWSMachineLearningErrorInvalidTag`, `AWSMachineLearningErrorTagLimitExceeded`, `AWSMachineLearningErrorResourceNotFound`, `AWSMachineLearningErrorInternalServer`. @see AWSMachineLearningAddTagsInput @see AWSMachineLearningAddTagsOutput */ - (void)addTags:(AWSMachineLearningAddTagsInput *)request completionHandler:(void (^ _Nullable)(AWSMachineLearningAddTagsOutput * _Nullable response, NSError * _Nullable error))completionHandler; /**

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 request A container for the necessary parameters to execute the CreateBatchPrediction service method. @return An instance of `AWSTask`. On successful execution, `task.result` will contain an instance of `AWSMachineLearningCreateBatchPredictionOutput`. On failed execution, `task.error` may contain an `NSError` with `AWSMachineLearningErrorDomain` domain and the following error code: `AWSMachineLearningErrorInvalidInput`, `AWSMachineLearningErrorInternalServer`, `AWSMachineLearningErrorIdempotentParameterMismatch`. @see AWSMachineLearningCreateBatchPredictionInput @see AWSMachineLearningCreateBatchPredictionOutput */ - (AWSTask *)createBatchPrediction:(AWSMachineLearningCreateBatchPredictionInput *)request; /**

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 request A container for the necessary parameters to execute the CreateBatchPrediction service method. @param completionHandler The completion handler to call when the load request is complete. `response` - A response object, or `nil` if the request failed. `error` - An error object that indicates why the request failed, or `nil` if the request was successful. On failed execution, `error` may contain an `NSError` with `AWSMachineLearningErrorDomain` domain and the following error code: `AWSMachineLearningErrorInvalidInput`, `AWSMachineLearningErrorInternalServer`, `AWSMachineLearningErrorIdempotentParameterMismatch`. @see AWSMachineLearningCreateBatchPredictionInput @see AWSMachineLearningCreateBatchPredictionOutput */ - (void)createBatchPrediction:(AWSMachineLearningCreateBatchPredictionInput *)request completionHandler:(void (^ _Nullable)(AWSMachineLearningCreateBatchPredictionOutput * _Nullable response, NSError * _Nullable error))completionHandler; /**

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 request A container for the necessary parameters to execute the CreateDataSourceFromRDS service method. @return An instance of `AWSTask`. On successful execution, `task.result` will contain an instance of `AWSMachineLearningCreateDataSourceFromRDSOutput`. On failed execution, `task.error` may contain an `NSError` with `AWSMachineLearningErrorDomain` domain and the following error code: `AWSMachineLearningErrorInvalidInput`, `AWSMachineLearningErrorInternalServer`, `AWSMachineLearningErrorIdempotentParameterMismatch`. @see AWSMachineLearningCreateDataSourceFromRDSInput @see AWSMachineLearningCreateDataSourceFromRDSOutput */ - (AWSTask *)createDataSourceFromRDS:(AWSMachineLearningCreateDataSourceFromRDSInput *)request; /**

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 request A container for the necessary parameters to execute the CreateDataSourceFromRDS service method. @param completionHandler The completion handler to call when the load request is complete. `response` - A response object, or `nil` if the request failed. `error` - An error object that indicates why the request failed, or `nil` if the request was successful. On failed execution, `error` may contain an `NSError` with `AWSMachineLearningErrorDomain` domain and the following error code: `AWSMachineLearningErrorInvalidInput`, `AWSMachineLearningErrorInternalServer`, `AWSMachineLearningErrorIdempotentParameterMismatch`. @see AWSMachineLearningCreateDataSourceFromRDSInput @see AWSMachineLearningCreateDataSourceFromRDSOutput */ - (void)createDataSourceFromRDS:(AWSMachineLearningCreateDataSourceFromRDSInput *)request completionHandler:(void (^ _Nullable)(AWSMachineLearningCreateDataSourceFromRDSOutput * _Nullable response, NSError * _Nullable error))completionHandler; /**

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 request A container for the necessary parameters to execute the CreateDataSourceFromRedshift service method. @return An instance of `AWSTask`. On successful execution, `task.result` will contain an instance of `AWSMachineLearningCreateDataSourceFromRedshiftOutput`. On failed execution, `task.error` may contain an `NSError` with `AWSMachineLearningErrorDomain` domain and the following error code: `AWSMachineLearningErrorInvalidInput`, `AWSMachineLearningErrorInternalServer`, `AWSMachineLearningErrorIdempotentParameterMismatch`. @see AWSMachineLearningCreateDataSourceFromRedshiftInput @see AWSMachineLearningCreateDataSourceFromRedshiftOutput */ - (AWSTask *)createDataSourceFromRedshift:(AWSMachineLearningCreateDataSourceFromRedshiftInput *)request; /**

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 request A container for the necessary parameters to execute the CreateDataSourceFromRedshift service method. @param completionHandler The completion handler to call when the load request is complete. `response` - A response object, or `nil` if the request failed. `error` - An error object that indicates why the request failed, or `nil` if the request was successful. On failed execution, `error` may contain an `NSError` with `AWSMachineLearningErrorDomain` domain and the following error code: `AWSMachineLearningErrorInvalidInput`, `AWSMachineLearningErrorInternalServer`, `AWSMachineLearningErrorIdempotentParameterMismatch`. @see AWSMachineLearningCreateDataSourceFromRedshiftInput @see AWSMachineLearningCreateDataSourceFromRedshiftOutput */ - (void)createDataSourceFromRedshift:(AWSMachineLearningCreateDataSourceFromRedshiftInput *)request completionHandler:(void (^ _Nullable)(AWSMachineLearningCreateDataSourceFromRedshiftOutput * _Nullable response, NSError * _Nullable error))completionHandler; /**

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 request A container for the necessary parameters to execute the CreateDataSourceFromS3 service method. @return An instance of `AWSTask`. On successful execution, `task.result` will contain an instance of `AWSMachineLearningCreateDataSourceFromS3Output`. On failed execution, `task.error` may contain an `NSError` with `AWSMachineLearningErrorDomain` domain and the following error code: `AWSMachineLearningErrorInvalidInput`, `AWSMachineLearningErrorInternalServer`, `AWSMachineLearningErrorIdempotentParameterMismatch`. @see AWSMachineLearningCreateDataSourceFromS3Input @see AWSMachineLearningCreateDataSourceFromS3Output */ - (AWSTask *)createDataSourceFromS3:(AWSMachineLearningCreateDataSourceFromS3Input *)request; /**

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 request A container for the necessary parameters to execute the CreateDataSourceFromS3 service method. @param completionHandler The completion handler to call when the load request is complete. `response` - A response object, or `nil` if the request failed. `error` - An error object that indicates why the request failed, or `nil` if the request was successful. On failed execution, `error` may contain an `NSError` with `AWSMachineLearningErrorDomain` domain and the following error code: `AWSMachineLearningErrorInvalidInput`, `AWSMachineLearningErrorInternalServer`, `AWSMachineLearningErrorIdempotentParameterMismatch`. @see AWSMachineLearningCreateDataSourceFromS3Input @see AWSMachineLearningCreateDataSourceFromS3Output */ - (void)createDataSourceFromS3:(AWSMachineLearningCreateDataSourceFromS3Input *)request completionHandler:(void (^ _Nullable)(AWSMachineLearningCreateDataSourceFromS3Output * _Nullable response, NSError * _Nullable error))completionHandler; /**

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 request A container for the necessary parameters to execute the CreateEvaluation service method. @return An instance of `AWSTask`. On successful execution, `task.result` will contain an instance of `AWSMachineLearningCreateEvaluationOutput`. On failed execution, `task.error` may contain an `NSError` with `AWSMachineLearningErrorDomain` domain and the following error code: `AWSMachineLearningErrorInvalidInput`, `AWSMachineLearningErrorInternalServer`, `AWSMachineLearningErrorIdempotentParameterMismatch`. @see AWSMachineLearningCreateEvaluationInput @see AWSMachineLearningCreateEvaluationOutput */ - (AWSTask *)createEvaluation:(AWSMachineLearningCreateEvaluationInput *)request; /**

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 request A container for the necessary parameters to execute the CreateEvaluation service method. @param completionHandler The completion handler to call when the load request is complete. `response` - A response object, or `nil` if the request failed. `error` - An error object that indicates why the request failed, or `nil` if the request was successful. On failed execution, `error` may contain an `NSError` with `AWSMachineLearningErrorDomain` domain and the following error code: `AWSMachineLearningErrorInvalidInput`, `AWSMachineLearningErrorInternalServer`, `AWSMachineLearningErrorIdempotentParameterMismatch`. @see AWSMachineLearningCreateEvaluationInput @see AWSMachineLearningCreateEvaluationOutput */ - (void)createEvaluation:(AWSMachineLearningCreateEvaluationInput *)request completionHandler:(void (^ _Nullable)(AWSMachineLearningCreateEvaluationOutput * _Nullable response, NSError * _Nullable error))completionHandler; /**

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 request A container for the necessary parameters to execute the CreateMLModel service method. @return An instance of `AWSTask`. On successful execution, `task.result` will contain an instance of `AWSMachineLearningCreateMLModelOutput`. On failed execution, `task.error` may contain an `NSError` with `AWSMachineLearningErrorDomain` domain and the following error code: `AWSMachineLearningErrorInvalidInput`, `AWSMachineLearningErrorInternalServer`, `AWSMachineLearningErrorIdempotentParameterMismatch`. @see AWSMachineLearningCreateMLModelInput @see AWSMachineLearningCreateMLModelOutput */ - (AWSTask *)createMLModel:(AWSMachineLearningCreateMLModelInput *)request; /**

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 request A container for the necessary parameters to execute the CreateMLModel service method. @param completionHandler The completion handler to call when the load request is complete. `response` - A response object, or `nil` if the request failed. `error` - An error object that indicates why the request failed, or `nil` if the request was successful. On failed execution, `error` may contain an `NSError` with `AWSMachineLearningErrorDomain` domain and the following error code: `AWSMachineLearningErrorInvalidInput`, `AWSMachineLearningErrorInternalServer`, `AWSMachineLearningErrorIdempotentParameterMismatch`. @see AWSMachineLearningCreateMLModelInput @see AWSMachineLearningCreateMLModelOutput */ - (void)createMLModel:(AWSMachineLearningCreateMLModelInput *)request completionHandler:(void (^ _Nullable)(AWSMachineLearningCreateMLModelOutput * _Nullable response, NSError * _Nullable error))completionHandler; /**

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 request A container for the necessary parameters to execute the CreateRealtimeEndpoint service method. @return An instance of `AWSTask`. On successful execution, `task.result` will contain an instance of `AWSMachineLearningCreateRealtimeEndpointOutput`. On failed execution, `task.error` may contain an `NSError` with `AWSMachineLearningErrorDomain` domain and the following error code: `AWSMachineLearningErrorInvalidInput`, `AWSMachineLearningErrorResourceNotFound`, `AWSMachineLearningErrorInternalServer`. @see AWSMachineLearningCreateRealtimeEndpointInput @see AWSMachineLearningCreateRealtimeEndpointOutput */ - (AWSTask *)createRealtimeEndpoint:(AWSMachineLearningCreateRealtimeEndpointInput *)request; /**

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 request A container for the necessary parameters to execute the CreateRealtimeEndpoint service method. @param completionHandler The completion handler to call when the load request is complete. `response` - A response object, or `nil` if the request failed. `error` - An error object that indicates why the request failed, or `nil` if the request was successful. On failed execution, `error` may contain an `NSError` with `AWSMachineLearningErrorDomain` domain and the following error code: `AWSMachineLearningErrorInvalidInput`, `AWSMachineLearningErrorResourceNotFound`, `AWSMachineLearningErrorInternalServer`. @see AWSMachineLearningCreateRealtimeEndpointInput @see AWSMachineLearningCreateRealtimeEndpointOutput */ - (void)createRealtimeEndpoint:(AWSMachineLearningCreateRealtimeEndpointInput *)request completionHandler:(void (^ _Nullable)(AWSMachineLearningCreateRealtimeEndpointOutput * _Nullable response, NSError * _Nullable error))completionHandler; /**

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 request A container for the necessary parameters to execute the DeleteBatchPrediction service method. @return An instance of `AWSTask`. On successful execution, `task.result` will contain an instance of `AWSMachineLearningDeleteBatchPredictionOutput`. On failed execution, `task.error` may contain an `NSError` with `AWSMachineLearningErrorDomain` domain and the following error code: `AWSMachineLearningErrorInvalidInput`, `AWSMachineLearningErrorResourceNotFound`, `AWSMachineLearningErrorInternalServer`. @see AWSMachineLearningDeleteBatchPredictionInput @see AWSMachineLearningDeleteBatchPredictionOutput */ - (AWSTask *)deleteBatchPrediction:(AWSMachineLearningDeleteBatchPredictionInput *)request; /**

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 request A container for the necessary parameters to execute the DeleteBatchPrediction service method. @param completionHandler The completion handler to call when the load request is complete. `response` - A response object, or `nil` if the request failed. `error` - An error object that indicates why the request failed, or `nil` if the request was successful. On failed execution, `error` may contain an `NSError` with `AWSMachineLearningErrorDomain` domain and the following error code: `AWSMachineLearningErrorInvalidInput`, `AWSMachineLearningErrorResourceNotFound`, `AWSMachineLearningErrorInternalServer`. @see AWSMachineLearningDeleteBatchPredictionInput @see AWSMachineLearningDeleteBatchPredictionOutput */ - (void)deleteBatchPrediction:(AWSMachineLearningDeleteBatchPredictionInput *)request completionHandler:(void (^ _Nullable)(AWSMachineLearningDeleteBatchPredictionOutput * _Nullable response, NSError * _Nullable error))completionHandler; /**

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 request A container for the necessary parameters to execute the DeleteDataSource service method. @return An instance of `AWSTask`. On successful execution, `task.result` will contain an instance of `AWSMachineLearningDeleteDataSourceOutput`. On failed execution, `task.error` may contain an `NSError` with `AWSMachineLearningErrorDomain` domain and the following error code: `AWSMachineLearningErrorInvalidInput`, `AWSMachineLearningErrorResourceNotFound`, `AWSMachineLearningErrorInternalServer`. @see AWSMachineLearningDeleteDataSourceInput @see AWSMachineLearningDeleteDataSourceOutput */ - (AWSTask *)deleteDataSource:(AWSMachineLearningDeleteDataSourceInput *)request; /**

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 request A container for the necessary parameters to execute the DeleteDataSource service method. @param completionHandler The completion handler to call when the load request is complete. `response` - A response object, or `nil` if the request failed. `error` - An error object that indicates why the request failed, or `nil` if the request was successful. On failed execution, `error` may contain an `NSError` with `AWSMachineLearningErrorDomain` domain and the following error code: `AWSMachineLearningErrorInvalidInput`, `AWSMachineLearningErrorResourceNotFound`, `AWSMachineLearningErrorInternalServer`. @see AWSMachineLearningDeleteDataSourceInput @see AWSMachineLearningDeleteDataSourceOutput */ - (void)deleteDataSource:(AWSMachineLearningDeleteDataSourceInput *)request completionHandler:(void (^ _Nullable)(AWSMachineLearningDeleteDataSourceOutput * _Nullable response, NSError * _Nullable error))completionHandler; /**

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 request A container for the necessary parameters to execute the DeleteEvaluation service method. @return An instance of `AWSTask`. On successful execution, `task.result` will contain an instance of `AWSMachineLearningDeleteEvaluationOutput`. On failed execution, `task.error` may contain an `NSError` with `AWSMachineLearningErrorDomain` domain and the following error code: `AWSMachineLearningErrorInvalidInput`, `AWSMachineLearningErrorResourceNotFound`, `AWSMachineLearningErrorInternalServer`. @see AWSMachineLearningDeleteEvaluationInput @see AWSMachineLearningDeleteEvaluationOutput */ - (AWSTask *)deleteEvaluation:(AWSMachineLearningDeleteEvaluationInput *)request; /**

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 request A container for the necessary parameters to execute the DeleteEvaluation service method. @param completionHandler The completion handler to call when the load request is complete. `response` - A response object, or `nil` if the request failed. `error` - An error object that indicates why the request failed, or `nil` if the request was successful. On failed execution, `error` may contain an `NSError` with `AWSMachineLearningErrorDomain` domain and the following error code: `AWSMachineLearningErrorInvalidInput`, `AWSMachineLearningErrorResourceNotFound`, `AWSMachineLearningErrorInternalServer`. @see AWSMachineLearningDeleteEvaluationInput @see AWSMachineLearningDeleteEvaluationOutput */ - (void)deleteEvaluation:(AWSMachineLearningDeleteEvaluationInput *)request completionHandler:(void (^ _Nullable)(AWSMachineLearningDeleteEvaluationOutput * _Nullable response, NSError * _Nullable error))completionHandler; /**

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 request A container for the necessary parameters to execute the DeleteMLModel service method. @return An instance of `AWSTask`. On successful execution, `task.result` will contain an instance of `AWSMachineLearningDeleteMLModelOutput`. On failed execution, `task.error` may contain an `NSError` with `AWSMachineLearningErrorDomain` domain and the following error code: `AWSMachineLearningErrorInvalidInput`, `AWSMachineLearningErrorResourceNotFound`, `AWSMachineLearningErrorInternalServer`. @see AWSMachineLearningDeleteMLModelInput @see AWSMachineLearningDeleteMLModelOutput */ - (AWSTask *)deleteMLModel:(AWSMachineLearningDeleteMLModelInput *)request; /**

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 request A container for the necessary parameters to execute the DeleteMLModel service method. @param completionHandler The completion handler to call when the load request is complete. `response` - A response object, or `nil` if the request failed. `error` - An error object that indicates why the request failed, or `nil` if the request was successful. On failed execution, `error` may contain an `NSError` with `AWSMachineLearningErrorDomain` domain and the following error code: `AWSMachineLearningErrorInvalidInput`, `AWSMachineLearningErrorResourceNotFound`, `AWSMachineLearningErrorInternalServer`. @see AWSMachineLearningDeleteMLModelInput @see AWSMachineLearningDeleteMLModelOutput */ - (void)deleteMLModel:(AWSMachineLearningDeleteMLModelInput *)request completionHandler:(void (^ _Nullable)(AWSMachineLearningDeleteMLModelOutput * _Nullable response, NSError * _Nullable error))completionHandler; /**

Deletes a real time endpoint of an MLModel.

@param request A container for the necessary parameters to execute the DeleteRealtimeEndpoint service method. @return An instance of `AWSTask`. On successful execution, `task.result` will contain an instance of `AWSMachineLearningDeleteRealtimeEndpointOutput`. On failed execution, `task.error` may contain an `NSError` with `AWSMachineLearningErrorDomain` domain and the following error code: `AWSMachineLearningErrorInvalidInput`, `AWSMachineLearningErrorResourceNotFound`, `AWSMachineLearningErrorInternalServer`. @see AWSMachineLearningDeleteRealtimeEndpointInput @see AWSMachineLearningDeleteRealtimeEndpointOutput */ - (AWSTask *)deleteRealtimeEndpoint:(AWSMachineLearningDeleteRealtimeEndpointInput *)request; /**

Deletes a real time endpoint of an MLModel.

@param request A container for the necessary parameters to execute the DeleteRealtimeEndpoint service method. @param completionHandler The completion handler to call when the load request is complete. `response` - A response object, or `nil` if the request failed. `error` - An error object that indicates why the request failed, or `nil` if the request was successful. On failed execution, `error` may contain an `NSError` with `AWSMachineLearningErrorDomain` domain and the following error code: `AWSMachineLearningErrorInvalidInput`, `AWSMachineLearningErrorResourceNotFound`, `AWSMachineLearningErrorInternalServer`. @see AWSMachineLearningDeleteRealtimeEndpointInput @see AWSMachineLearningDeleteRealtimeEndpointOutput */ - (void)deleteRealtimeEndpoint:(AWSMachineLearningDeleteRealtimeEndpointInput *)request completionHandler:(void (^ _Nullable)(AWSMachineLearningDeleteRealtimeEndpointOutput * _Nullable response, NSError * _Nullable error))completionHandler; /**

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 request A container for the necessary parameters to execute the DeleteTags service method. @return An instance of `AWSTask`. On successful execution, `task.result` will contain an instance of `AWSMachineLearningDeleteTagsOutput`. On failed execution, `task.error` may contain an `NSError` with `AWSMachineLearningErrorDomain` domain and the following error code: `AWSMachineLearningErrorInvalidInput`, `AWSMachineLearningErrorInvalidTag`, `AWSMachineLearningErrorResourceNotFound`, `AWSMachineLearningErrorInternalServer`. @see AWSMachineLearningDeleteTagsInput @see AWSMachineLearningDeleteTagsOutput */ - (AWSTask *)deleteTags:(AWSMachineLearningDeleteTagsInput *)request; /**

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 request A container for the necessary parameters to execute the DeleteTags service method. @param completionHandler The completion handler to call when the load request is complete. `response` - A response object, or `nil` if the request failed. `error` - An error object that indicates why the request failed, or `nil` if the request was successful. On failed execution, `error` may contain an `NSError` with `AWSMachineLearningErrorDomain` domain and the following error code: `AWSMachineLearningErrorInvalidInput`, `AWSMachineLearningErrorInvalidTag`, `AWSMachineLearningErrorResourceNotFound`, `AWSMachineLearningErrorInternalServer`. @see AWSMachineLearningDeleteTagsInput @see AWSMachineLearningDeleteTagsOutput */ - (void)deleteTags:(AWSMachineLearningDeleteTagsInput *)request completionHandler:(void (^ _Nullable)(AWSMachineLearningDeleteTagsOutput * _Nullable response, NSError * _Nullable error))completionHandler; /**

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

@param request A container for the necessary parameters to execute the DescribeBatchPredictions service method. @return An instance of `AWSTask`. On successful execution, `task.result` will contain an instance of `AWSMachineLearningDescribeBatchPredictionsOutput`. On failed execution, `task.error` may contain an `NSError` with `AWSMachineLearningErrorDomain` domain and the following error code: `AWSMachineLearningErrorInvalidInput`, `AWSMachineLearningErrorInternalServer`. @see AWSMachineLearningDescribeBatchPredictionsInput @see AWSMachineLearningDescribeBatchPredictionsOutput */ - (AWSTask *)describeBatchPredictions:(AWSMachineLearningDescribeBatchPredictionsInput *)request; /**

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

@param request A container for the necessary parameters to execute the DescribeBatchPredictions service method. @param completionHandler The completion handler to call when the load request is complete. `response` - A response object, or `nil` if the request failed. `error` - An error object that indicates why the request failed, or `nil` if the request was successful. On failed execution, `error` may contain an `NSError` with `AWSMachineLearningErrorDomain` domain and the following error code: `AWSMachineLearningErrorInvalidInput`, `AWSMachineLearningErrorInternalServer`. @see AWSMachineLearningDescribeBatchPredictionsInput @see AWSMachineLearningDescribeBatchPredictionsOutput */ - (void)describeBatchPredictions:(AWSMachineLearningDescribeBatchPredictionsInput *)request completionHandler:(void (^ _Nullable)(AWSMachineLearningDescribeBatchPredictionsOutput * _Nullable response, NSError * _Nullable error))completionHandler; /**

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

@param request A container for the necessary parameters to execute the DescribeDataSources service method. @return An instance of `AWSTask`. On successful execution, `task.result` will contain an instance of `AWSMachineLearningDescribeDataSourcesOutput`. On failed execution, `task.error` may contain an `NSError` with `AWSMachineLearningErrorDomain` domain and the following error code: `AWSMachineLearningErrorInvalidInput`, `AWSMachineLearningErrorInternalServer`. @see AWSMachineLearningDescribeDataSourcesInput @see AWSMachineLearningDescribeDataSourcesOutput */ - (AWSTask *)describeDataSources:(AWSMachineLearningDescribeDataSourcesInput *)request; /**

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

@param request A container for the necessary parameters to execute the DescribeDataSources service method. @param completionHandler The completion handler to call when the load request is complete. `response` - A response object, or `nil` if the request failed. `error` - An error object that indicates why the request failed, or `nil` if the request was successful. On failed execution, `error` may contain an `NSError` with `AWSMachineLearningErrorDomain` domain and the following error code: `AWSMachineLearningErrorInvalidInput`, `AWSMachineLearningErrorInternalServer`. @see AWSMachineLearningDescribeDataSourcesInput @see AWSMachineLearningDescribeDataSourcesOutput */ - (void)describeDataSources:(AWSMachineLearningDescribeDataSourcesInput *)request completionHandler:(void (^ _Nullable)(AWSMachineLearningDescribeDataSourcesOutput * _Nullable response, NSError * _Nullable error))completionHandler; /**

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

@param request A container for the necessary parameters to execute the DescribeEvaluations service method. @return An instance of `AWSTask`. On successful execution, `task.result` will contain an instance of `AWSMachineLearningDescribeEvaluationsOutput`. On failed execution, `task.error` may contain an `NSError` with `AWSMachineLearningErrorDomain` domain and the following error code: `AWSMachineLearningErrorInvalidInput`, `AWSMachineLearningErrorInternalServer`. @see AWSMachineLearningDescribeEvaluationsInput @see AWSMachineLearningDescribeEvaluationsOutput */ - (AWSTask *)describeEvaluations:(AWSMachineLearningDescribeEvaluationsInput *)request; /**

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

@param request A container for the necessary parameters to execute the DescribeEvaluations service method. @param completionHandler The completion handler to call when the load request is complete. `response` - A response object, or `nil` if the request failed. `error` - An error object that indicates why the request failed, or `nil` if the request was successful. On failed execution, `error` may contain an `NSError` with `AWSMachineLearningErrorDomain` domain and the following error code: `AWSMachineLearningErrorInvalidInput`, `AWSMachineLearningErrorInternalServer`. @see AWSMachineLearningDescribeEvaluationsInput @see AWSMachineLearningDescribeEvaluationsOutput */ - (void)describeEvaluations:(AWSMachineLearningDescribeEvaluationsInput *)request completionHandler:(void (^ _Nullable)(AWSMachineLearningDescribeEvaluationsOutput * _Nullable response, NSError * _Nullable error))completionHandler; /**

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

@param request A container for the necessary parameters to execute the DescribeMLModels service method. @return An instance of `AWSTask`. On successful execution, `task.result` will contain an instance of `AWSMachineLearningDescribeMLModelsOutput`. On failed execution, `task.error` may contain an `NSError` with `AWSMachineLearningErrorDomain` domain and the following error code: `AWSMachineLearningErrorInvalidInput`, `AWSMachineLearningErrorInternalServer`. @see AWSMachineLearningDescribeMLModelsInput @see AWSMachineLearningDescribeMLModelsOutput */ - (AWSTask *)describeMLModels:(AWSMachineLearningDescribeMLModelsInput *)request; /**

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

@param request A container for the necessary parameters to execute the DescribeMLModels service method. @param completionHandler The completion handler to call when the load request is complete. `response` - A response object, or `nil` if the request failed. `error` - An error object that indicates why the request failed, or `nil` if the request was successful. On failed execution, `error` may contain an `NSError` with `AWSMachineLearningErrorDomain` domain and the following error code: `AWSMachineLearningErrorInvalidInput`, `AWSMachineLearningErrorInternalServer`. @see AWSMachineLearningDescribeMLModelsInput @see AWSMachineLearningDescribeMLModelsOutput */ - (void)describeMLModels:(AWSMachineLearningDescribeMLModelsInput *)request completionHandler:(void (^ _Nullable)(AWSMachineLearningDescribeMLModelsOutput * _Nullable response, NSError * _Nullable error))completionHandler; /**

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

@param request A container for the necessary parameters to execute the DescribeTags service method. @return An instance of `AWSTask`. On successful execution, `task.result` will contain an instance of `AWSMachineLearningDescribeTagsOutput`. On failed execution, `task.error` may contain an `NSError` with `AWSMachineLearningErrorDomain` domain and the following error code: `AWSMachineLearningErrorInvalidInput`, `AWSMachineLearningErrorResourceNotFound`, `AWSMachineLearningErrorInternalServer`. @see AWSMachineLearningDescribeTagsInput @see AWSMachineLearningDescribeTagsOutput */ - (AWSTask *)describeTags:(AWSMachineLearningDescribeTagsInput *)request; /**

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

@param request A container for the necessary parameters to execute the DescribeTags service method. @param completionHandler The completion handler to call when the load request is complete. `response` - A response object, or `nil` if the request failed. `error` - An error object that indicates why the request failed, or `nil` if the request was successful. On failed execution, `error` may contain an `NSError` with `AWSMachineLearningErrorDomain` domain and the following error code: `AWSMachineLearningErrorInvalidInput`, `AWSMachineLearningErrorResourceNotFound`, `AWSMachineLearningErrorInternalServer`. @see AWSMachineLearningDescribeTagsInput @see AWSMachineLearningDescribeTagsOutput */ - (void)describeTags:(AWSMachineLearningDescribeTagsInput *)request completionHandler:(void (^ _Nullable)(AWSMachineLearningDescribeTagsOutput * _Nullable response, NSError * _Nullable error))completionHandler; /**

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

@param request A container for the necessary parameters to execute the GetBatchPrediction service method. @return An instance of `AWSTask`. On successful execution, `task.result` will contain an instance of `AWSMachineLearningGetBatchPredictionOutput`. On failed execution, `task.error` may contain an `NSError` with `AWSMachineLearningErrorDomain` domain and the following error code: `AWSMachineLearningErrorInvalidInput`, `AWSMachineLearningErrorResourceNotFound`, `AWSMachineLearningErrorInternalServer`. @see AWSMachineLearningGetBatchPredictionInput @see AWSMachineLearningGetBatchPredictionOutput */ - (AWSTask *)getBatchPrediction:(AWSMachineLearningGetBatchPredictionInput *)request; /**

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

@param request A container for the necessary parameters to execute the GetBatchPrediction service method. @param completionHandler The completion handler to call when the load request is complete. `response` - A response object, or `nil` if the request failed. `error` - An error object that indicates why the request failed, or `nil` if the request was successful. On failed execution, `error` may contain an `NSError` with `AWSMachineLearningErrorDomain` domain and the following error code: `AWSMachineLearningErrorInvalidInput`, `AWSMachineLearningErrorResourceNotFound`, `AWSMachineLearningErrorInternalServer`. @see AWSMachineLearningGetBatchPredictionInput @see AWSMachineLearningGetBatchPredictionOutput */ - (void)getBatchPrediction:(AWSMachineLearningGetBatchPredictionInput *)request completionHandler:(void (^ _Nullable)(AWSMachineLearningGetBatchPredictionOutput * _Nullable response, NSError * _Nullable error))completionHandler; /**

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 request A container for the necessary parameters to execute the GetDataSource service method. @return An instance of `AWSTask`. On successful execution, `task.result` will contain an instance of `AWSMachineLearningGetDataSourceOutput`. On failed execution, `task.error` may contain an `NSError` with `AWSMachineLearningErrorDomain` domain and the following error code: `AWSMachineLearningErrorInvalidInput`, `AWSMachineLearningErrorResourceNotFound`, `AWSMachineLearningErrorInternalServer`. @see AWSMachineLearningGetDataSourceInput @see AWSMachineLearningGetDataSourceOutput */ - (AWSTask *)getDataSource:(AWSMachineLearningGetDataSourceInput *)request; /**

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 request A container for the necessary parameters to execute the GetDataSource service method. @param completionHandler The completion handler to call when the load request is complete. `response` - A response object, or `nil` if the request failed. `error` - An error object that indicates why the request failed, or `nil` if the request was successful. On failed execution, `error` may contain an `NSError` with `AWSMachineLearningErrorDomain` domain and the following error code: `AWSMachineLearningErrorInvalidInput`, `AWSMachineLearningErrorResourceNotFound`, `AWSMachineLearningErrorInternalServer`. @see AWSMachineLearningGetDataSourceInput @see AWSMachineLearningGetDataSourceOutput */ - (void)getDataSource:(AWSMachineLearningGetDataSourceInput *)request completionHandler:(void (^ _Nullable)(AWSMachineLearningGetDataSourceOutput * _Nullable response, NSError * _Nullable error))completionHandler; /**

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

@param request A container for the necessary parameters to execute the GetEvaluation service method. @return An instance of `AWSTask`. On successful execution, `task.result` will contain an instance of `AWSMachineLearningGetEvaluationOutput`. On failed execution, `task.error` may contain an `NSError` with `AWSMachineLearningErrorDomain` domain and the following error code: `AWSMachineLearningErrorInvalidInput`, `AWSMachineLearningErrorResourceNotFound`, `AWSMachineLearningErrorInternalServer`. @see AWSMachineLearningGetEvaluationInput @see AWSMachineLearningGetEvaluationOutput */ - (AWSTask *)getEvaluation:(AWSMachineLearningGetEvaluationInput *)request; /**

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

@param request A container for the necessary parameters to execute the GetEvaluation service method. @param completionHandler The completion handler to call when the load request is complete. `response` - A response object, or `nil` if the request failed. `error` - An error object that indicates why the request failed, or `nil` if the request was successful. On failed execution, `error` may contain an `NSError` with `AWSMachineLearningErrorDomain` domain and the following error code: `AWSMachineLearningErrorInvalidInput`, `AWSMachineLearningErrorResourceNotFound`, `AWSMachineLearningErrorInternalServer`. @see AWSMachineLearningGetEvaluationInput @see AWSMachineLearningGetEvaluationOutput */ - (void)getEvaluation:(AWSMachineLearningGetEvaluationInput *)request completionHandler:(void (^ _Nullable)(AWSMachineLearningGetEvaluationOutput * _Nullable response, NSError * _Nullable error))completionHandler; /**

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 request A container for the necessary parameters to execute the GetMLModel service method. @return An instance of `AWSTask`. On successful execution, `task.result` will contain an instance of `AWSMachineLearningGetMLModelOutput`. On failed execution, `task.error` may contain an `NSError` with `AWSMachineLearningErrorDomain` domain and the following error code: `AWSMachineLearningErrorInvalidInput`, `AWSMachineLearningErrorResourceNotFound`, `AWSMachineLearningErrorInternalServer`. @see AWSMachineLearningGetMLModelInput @see AWSMachineLearningGetMLModelOutput */ - (AWSTask *)getMLModel:(AWSMachineLearningGetMLModelInput *)request; /**

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 request A container for the necessary parameters to execute the GetMLModel service method. @param completionHandler The completion handler to call when the load request is complete. `response` - A response object, or `nil` if the request failed. `error` - An error object that indicates why the request failed, or `nil` if the request was successful. On failed execution, `error` may contain an `NSError` with `AWSMachineLearningErrorDomain` domain and the following error code: `AWSMachineLearningErrorInvalidInput`, `AWSMachineLearningErrorResourceNotFound`, `AWSMachineLearningErrorInternalServer`. @see AWSMachineLearningGetMLModelInput @see AWSMachineLearningGetMLModelOutput */ - (void)getMLModel:(AWSMachineLearningGetMLModelInput *)request completionHandler:(void (^ _Nullable)(AWSMachineLearningGetMLModelOutput * _Nullable response, NSError * _Nullable error))completionHandler; /**

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 request A container for the necessary parameters to execute the Predict service method. @return An instance of `AWSTask`. On successful execution, `task.result` will contain an instance of `AWSMachineLearningPredictOutput`. On failed execution, `task.error` may contain an `NSError` with `AWSMachineLearningErrorDomain` domain and the following error code: `AWSMachineLearningErrorInvalidInput`, `AWSMachineLearningErrorResourceNotFound`, `AWSMachineLearningErrorLimitExceeded`, `AWSMachineLearningErrorInternalServer`, `AWSMachineLearningErrorPredictorNotMounted`. @see AWSMachineLearningPredictInput @see AWSMachineLearningPredictOutput */ - (AWSTask *)predict:(AWSMachineLearningPredictInput *)request; /**

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 request A container for the necessary parameters to execute the Predict service method. @param completionHandler The completion handler to call when the load request is complete. `response` - A response object, or `nil` if the request failed. `error` - An error object that indicates why the request failed, or `nil` if the request was successful. On failed execution, `error` may contain an `NSError` with `AWSMachineLearningErrorDomain` domain and the following error code: `AWSMachineLearningErrorInvalidInput`, `AWSMachineLearningErrorResourceNotFound`, `AWSMachineLearningErrorLimitExceeded`, `AWSMachineLearningErrorInternalServer`, `AWSMachineLearningErrorPredictorNotMounted`. @see AWSMachineLearningPredictInput @see AWSMachineLearningPredictOutput */ - (void)predict:(AWSMachineLearningPredictInput *)request completionHandler:(void (^ _Nullable)(AWSMachineLearningPredictOutput * _Nullable response, NSError * _Nullable error))completionHandler; /**

Updates the BatchPredictionName of a BatchPrediction.

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

@param request A container for the necessary parameters to execute the UpdateBatchPrediction service method. @return An instance of `AWSTask`. On successful execution, `task.result` will contain an instance of `AWSMachineLearningUpdateBatchPredictionOutput`. On failed execution, `task.error` may contain an `NSError` with `AWSMachineLearningErrorDomain` domain and the following error code: `AWSMachineLearningErrorInvalidInput`, `AWSMachineLearningErrorResourceNotFound`, `AWSMachineLearningErrorInternalServer`. @see AWSMachineLearningUpdateBatchPredictionInput @see AWSMachineLearningUpdateBatchPredictionOutput */ - (AWSTask *)updateBatchPrediction:(AWSMachineLearningUpdateBatchPredictionInput *)request; /**

Updates the BatchPredictionName of a BatchPrediction.

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

@param request A container for the necessary parameters to execute the UpdateBatchPrediction service method. @param completionHandler The completion handler to call when the load request is complete. `response` - A response object, or `nil` if the request failed. `error` - An error object that indicates why the request failed, or `nil` if the request was successful. On failed execution, `error` may contain an `NSError` with `AWSMachineLearningErrorDomain` domain and the following error code: `AWSMachineLearningErrorInvalidInput`, `AWSMachineLearningErrorResourceNotFound`, `AWSMachineLearningErrorInternalServer`. @see AWSMachineLearningUpdateBatchPredictionInput @see AWSMachineLearningUpdateBatchPredictionOutput */ - (void)updateBatchPrediction:(AWSMachineLearningUpdateBatchPredictionInput *)request completionHandler:(void (^ _Nullable)(AWSMachineLearningUpdateBatchPredictionOutput * _Nullable response, NSError * _Nullable error))completionHandler; /**

Updates the DataSourceName of a DataSource.

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

@param request A container for the necessary parameters to execute the UpdateDataSource service method. @return An instance of `AWSTask`. On successful execution, `task.result` will contain an instance of `AWSMachineLearningUpdateDataSourceOutput`. On failed execution, `task.error` may contain an `NSError` with `AWSMachineLearningErrorDomain` domain and the following error code: `AWSMachineLearningErrorInvalidInput`, `AWSMachineLearningErrorResourceNotFound`, `AWSMachineLearningErrorInternalServer`. @see AWSMachineLearningUpdateDataSourceInput @see AWSMachineLearningUpdateDataSourceOutput */ - (AWSTask *)updateDataSource:(AWSMachineLearningUpdateDataSourceInput *)request; /**

Updates the DataSourceName of a DataSource.

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

@param request A container for the necessary parameters to execute the UpdateDataSource service method. @param completionHandler The completion handler to call when the load request is complete. `response` - A response object, or `nil` if the request failed. `error` - An error object that indicates why the request failed, or `nil` if the request was successful. On failed execution, `error` may contain an `NSError` with `AWSMachineLearningErrorDomain` domain and the following error code: `AWSMachineLearningErrorInvalidInput`, `AWSMachineLearningErrorResourceNotFound`, `AWSMachineLearningErrorInternalServer`. @see AWSMachineLearningUpdateDataSourceInput @see AWSMachineLearningUpdateDataSourceOutput */ - (void)updateDataSource:(AWSMachineLearningUpdateDataSourceInput *)request completionHandler:(void (^ _Nullable)(AWSMachineLearningUpdateDataSourceOutput * _Nullable response, NSError * _Nullable error))completionHandler; /**

Updates the EvaluationName of an Evaluation.

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

@param request A container for the necessary parameters to execute the UpdateEvaluation service method. @return An instance of `AWSTask`. On successful execution, `task.result` will contain an instance of `AWSMachineLearningUpdateEvaluationOutput`. On failed execution, `task.error` may contain an `NSError` with `AWSMachineLearningErrorDomain` domain and the following error code: `AWSMachineLearningErrorInvalidInput`, `AWSMachineLearningErrorResourceNotFound`, `AWSMachineLearningErrorInternalServer`. @see AWSMachineLearningUpdateEvaluationInput @see AWSMachineLearningUpdateEvaluationOutput */ - (AWSTask *)updateEvaluation:(AWSMachineLearningUpdateEvaluationInput *)request; /**

Updates the EvaluationName of an Evaluation.

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

@param request A container for the necessary parameters to execute the UpdateEvaluation service method. @param completionHandler The completion handler to call when the load request is complete. `response` - A response object, or `nil` if the request failed. `error` - An error object that indicates why the request failed, or `nil` if the request was successful. On failed execution, `error` may contain an `NSError` with `AWSMachineLearningErrorDomain` domain and the following error code: `AWSMachineLearningErrorInvalidInput`, `AWSMachineLearningErrorResourceNotFound`, `AWSMachineLearningErrorInternalServer`. @see AWSMachineLearningUpdateEvaluationInput @see AWSMachineLearningUpdateEvaluationOutput */ - (void)updateEvaluation:(AWSMachineLearningUpdateEvaluationInput *)request completionHandler:(void (^ _Nullable)(AWSMachineLearningUpdateEvaluationOutput * _Nullable response, NSError * _Nullable error))completionHandler; /**

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 request A container for the necessary parameters to execute the UpdateMLModel service method. @return An instance of `AWSTask`. On successful execution, `task.result` will contain an instance of `AWSMachineLearningUpdateMLModelOutput`. On failed execution, `task.error` may contain an `NSError` with `AWSMachineLearningErrorDomain` domain and the following error code: `AWSMachineLearningErrorInvalidInput`, `AWSMachineLearningErrorResourceNotFound`, `AWSMachineLearningErrorInternalServer`. @see AWSMachineLearningUpdateMLModelInput @see AWSMachineLearningUpdateMLModelOutput */ - (AWSTask *)updateMLModel:(AWSMachineLearningUpdateMLModelInput *)request; /**

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 request A container for the necessary parameters to execute the UpdateMLModel service method. @param completionHandler The completion handler to call when the load request is complete. `response` - A response object, or `nil` if the request failed. `error` - An error object that indicates why the request failed, or `nil` if the request was successful. On failed execution, `error` may contain an `NSError` with `AWSMachineLearningErrorDomain` domain and the following error code: `AWSMachineLearningErrorInvalidInput`, `AWSMachineLearningErrorResourceNotFound`, `AWSMachineLearningErrorInternalServer`. @see AWSMachineLearningUpdateMLModelInput @see AWSMachineLearningUpdateMLModelOutput */ - (void)updateMLModel:(AWSMachineLearningUpdateMLModelInput *)request completionHandler:(void (^ _Nullable)(AWSMachineLearningUpdateMLModelOutput * _Nullable response, NSError * _Nullable error))completionHandler; @end NS_ASSUME_NONNULL_END