AWSMachineLearning Class Reference

Inherits from AWSService : NSObject
Declared in AWSMachineLearningService.h
AWSMachineLearningService.m

Overview

Definition of the public APIs exposed by Amazon Machine Learning

  configuration

The service configuration used to instantiate this service client.

@property (nonatomic, strong, readonly) AWSServiceConfiguration *configuration

Discussion

Warning: Once the client is instantiated, do not modify the configuration object. It may cause unspecified behaviors.

Declared In

AWSMachineLearningService.h

+ defaultMachineLearning

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.

+ (instancetype)defaultMachineLearning

Return Value

The default service client.

Discussion

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];

Declared In

AWSMachineLearningService.h

+ registerMachineLearningWithConfiguration:forKey:

Creates a service client with the given service configuration and registers it for the key.

+ (void)registerMachineLearningWithConfiguration:(AWSServiceConfiguration *)configuration forKey:(NSString *)key

Parameters

configuration

A service configuration object.

key

A string to identify the service client.

Discussion

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.

Declared In

AWSMachineLearningService.h

+ MachineLearningForKey:

Retrieves the service client associated with the key. You need to call + registerMachineLearningWithConfiguration:forKey: before invoking this method.

+ (instancetype)MachineLearningForKey:(NSString *)key

Parameters

key

A string to identify the service client.

Return Value

An instance of the service client.

Discussion

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"];

Declared In

AWSMachineLearningService.h

+ removeMachineLearningForKey:

Removes the service client associated with the key and release it.

+ (void)removeMachineLearningForKey:(NSString *)key

Parameters

key

A string to identify the service client.

Discussion

Warning: Before calling this method, make sure no method is running on this client.

Declared In

AWSMachineLearningService.h

– addTags:

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.

- (AWSTask<AWSMachineLearningAddTagsOutput*> *)addTags:(AWSMachineLearningAddTagsInput *)request

Parameters

request

A container for the necessary parameters to execute the AddTags service method.

Return Value

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.

Declared In

AWSMachineLearningService.h

– addTags:completionHandler:

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.

- (void)addTags:(AWSMachineLearningAddTagsInput *)request completionHandler:(void ( ^ _Nullable ) ( AWSMachineLearningAddTagsOutput *_Nullable response , NSError *_Nullable error ))completionHandler

Parameters

request

A container for the necessary parameters to execute the AddTags service method.

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.

Declared In

AWSMachineLearningService.h

– createBatchPrediction:

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.

- (AWSTask<AWSMachineLearningCreateBatchPredictionOutput*> *)createBatchPrediction:(AWSMachineLearningCreateBatchPredictionInput *)request

Parameters

request

A container for the necessary parameters to execute the CreateBatchPrediction service method.

Return Value

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.

Declared In

AWSMachineLearningService.h

– createBatchPrediction: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.

- (void)createBatchPrediction:(AWSMachineLearningCreateBatchPredictionInput *)request completionHandler:(void ( ^ _Nullable ) ( AWSMachineLearningCreateBatchPredictionOutput *_Nullable response , NSError *_Nullable error ))completionHandler

Parameters

request

A container for the necessary parameters to execute the CreateBatchPrediction service method.

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.

Declared In

AWSMachineLearningService.h

– createDataSourceFromRDS:

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.

- (AWSTask<AWSMachineLearningCreateDataSourceFromRDSOutput*> *)createDataSourceFromRDS:(AWSMachineLearningCreateDataSourceFromRDSInput *)request

Parameters

request

A container for the necessary parameters to execute the CreateDataSourceFromRDS service method.

Return Value

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.

Declared In

AWSMachineLearningService.h

– createDataSourceFromRDS: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.

- (void)createDataSourceFromRDS:(AWSMachineLearningCreateDataSourceFromRDSInput *)request completionHandler:(void ( ^ _Nullable ) ( AWSMachineLearningCreateDataSourceFromRDSOutput *_Nullable response , NSError *_Nullable error ))completionHandler

Parameters

request

A container for the necessary parameters to execute the CreateDataSourceFromRDS service method.

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.

Declared In

AWSMachineLearningService.h

– createDataSourceFromRedshift:

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.

<?oxy_insert_start author=“laurama” timestamp=“20160406T153842-0700”>

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.

<?oxy_insert_end>

- (AWSTask<AWSMachineLearningCreateDataSourceFromRedshiftOutput*> *)createDataSourceFromRedshift:(AWSMachineLearningCreateDataSourceFromRedshiftInput *)request

Parameters

request

A container for the necessary parameters to execute the CreateDataSourceFromRedshift service method.

Return Value

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.

Declared In

AWSMachineLearningService.h

– createDataSourceFromRedshift: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.

<?oxy_insert_start author=“laurama” timestamp=“20160406T153842-0700”>

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.

<?oxy_insert_end>

- (void)createDataSourceFromRedshift:(AWSMachineLearningCreateDataSourceFromRedshiftInput *)request completionHandler:(void ( ^ _Nullable ) ( AWSMachineLearningCreateDataSourceFromRedshiftOutput *_Nullable response , NSError *_Nullable error ))completionHandler

Parameters

request

A container for the necessary parameters to execute the CreateDataSourceFromRedshift service method.

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.

Declared In

AWSMachineLearningService.h

– createDataSourceFromS3:

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.

- (AWSTask<AWSMachineLearningCreateDataSourceFromS3Output*> *)createDataSourceFromS3:(AWSMachineLearningCreateDataSourceFromS3Input *)request

Parameters

request

A container for the necessary parameters to execute the CreateDataSourceFromS3 service method.

Return Value

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.

Declared In

AWSMachineLearningService.h

– createDataSourceFromS3: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.

- (void)createDataSourceFromS3:(AWSMachineLearningCreateDataSourceFromS3Input *)request completionHandler:(void ( ^ _Nullable ) ( AWSMachineLearningCreateDataSourceFromS3Output *_Nullable response , NSError *_Nullable error ))completionHandler

Parameters

request

A container for the necessary parameters to execute the CreateDataSourceFromS3 service method.

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.

Declared In

AWSMachineLearningService.h

– createEvaluation:

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.

- (AWSTask<AWSMachineLearningCreateEvaluationOutput*> *)createEvaluation:(AWSMachineLearningCreateEvaluationInput *)request

Parameters

request

A container for the necessary parameters to execute the CreateEvaluation service method.

Return Value

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.

Declared In

AWSMachineLearningService.h

– createEvaluation: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.

- (void)createEvaluation:(AWSMachineLearningCreateEvaluationInput *)request completionHandler:(void ( ^ _Nullable ) ( AWSMachineLearningCreateEvaluationOutput *_Nullable response , NSError *_Nullable error ))completionHandler

Parameters

request

A container for the necessary parameters to execute the CreateEvaluation service method.

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.

Declared In

AWSMachineLearningService.h

– createMLModel:

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.

- (AWSTask<AWSMachineLearningCreateMLModelOutput*> *)createMLModel:(AWSMachineLearningCreateMLModelInput *)request

Parameters

request

A container for the necessary parameters to execute the CreateMLModel service method.

Return Value

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.

Declared In

AWSMachineLearningService.h

– createMLModel: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.

- (void)createMLModel:(AWSMachineLearningCreateMLModelInput *)request completionHandler:(void ( ^ _Nullable ) ( AWSMachineLearningCreateMLModelOutput *_Nullable response , NSError *_Nullable error ))completionHandler

Parameters

request

A container for the necessary parameters to execute the CreateMLModel service method.

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.

Declared In

AWSMachineLearningService.h

– createRealtimeEndpoint:

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.

- (AWSTask<AWSMachineLearningCreateRealtimeEndpointOutput*> *)createRealtimeEndpoint:(AWSMachineLearningCreateRealtimeEndpointInput *)request

Parameters

request

A container for the necessary parameters to execute the CreateRealtimeEndpoint service method.

Return Value

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.

Declared In

AWSMachineLearningService.h

– createRealtimeEndpoint: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.

- (void)createRealtimeEndpoint:(AWSMachineLearningCreateRealtimeEndpointInput *)request completionHandler:(void ( ^ _Nullable ) ( AWSMachineLearningCreateRealtimeEndpointOutput *_Nullable response , NSError *_Nullable error ))completionHandler

Parameters

request

A container for the necessary parameters to execute the CreateRealtimeEndpoint service method.

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.

Declared In

AWSMachineLearningService.h

– deleteBatchPrediction:

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.

- (AWSTask<AWSMachineLearningDeleteBatchPredictionOutput*> *)deleteBatchPrediction:(AWSMachineLearningDeleteBatchPredictionInput *)request

Parameters

request

A container for the necessary parameters to execute the DeleteBatchPrediction service method.

Return Value

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.

Declared In

AWSMachineLearningService.h

– deleteBatchPrediction: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.

- (void)deleteBatchPrediction:(AWSMachineLearningDeleteBatchPredictionInput *)request completionHandler:(void ( ^ _Nullable ) ( AWSMachineLearningDeleteBatchPredictionOutput *_Nullable response , NSError *_Nullable error ))completionHandler

Parameters

request

A container for the necessary parameters to execute the DeleteBatchPrediction service method.

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.

Declared In

AWSMachineLearningService.h

– deleteDataSource:

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.

- (AWSTask<AWSMachineLearningDeleteDataSourceOutput*> *)deleteDataSource:(AWSMachineLearningDeleteDataSourceInput *)request

Parameters

request

A container for the necessary parameters to execute the DeleteDataSource service method.

Return Value

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.

Declared In

AWSMachineLearningService.h

– deleteDataSource: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.

- (void)deleteDataSource:(AWSMachineLearningDeleteDataSourceInput *)request completionHandler:(void ( ^ _Nullable ) ( AWSMachineLearningDeleteDataSourceOutput *_Nullable response , NSError *_Nullable error ))completionHandler

Parameters

request

A container for the necessary parameters to execute the DeleteDataSource service method.

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.

Declared In

AWSMachineLearningService.h

– deleteEvaluation:

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.

- (AWSTask<AWSMachineLearningDeleteEvaluationOutput*> *)deleteEvaluation:(AWSMachineLearningDeleteEvaluationInput *)request

Parameters

request

A container for the necessary parameters to execute the DeleteEvaluation service method.

Return Value

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.

Declared In

AWSMachineLearningService.h

– deleteEvaluation: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.

- (void)deleteEvaluation:(AWSMachineLearningDeleteEvaluationInput *)request completionHandler:(void ( ^ _Nullable ) ( AWSMachineLearningDeleteEvaluationOutput *_Nullable response , NSError *_Nullable error ))completionHandler

Parameters

request

A container for the necessary parameters to execute the DeleteEvaluation service method.

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.

Declared In

AWSMachineLearningService.h

– deleteMLModel:

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.

- (AWSTask<AWSMachineLearningDeleteMLModelOutput*> *)deleteMLModel:(AWSMachineLearningDeleteMLModelInput *)request

Parameters

request

A container for the necessary parameters to execute the DeleteMLModel service method.

Return Value

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.

Declared In

AWSMachineLearningService.h

– deleteMLModel: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.

- (void)deleteMLModel:(AWSMachineLearningDeleteMLModelInput *)request completionHandler:(void ( ^ _Nullable ) ( AWSMachineLearningDeleteMLModelOutput *_Nullable response , NSError *_Nullable error ))completionHandler

Parameters

request

A container for the necessary parameters to execute the DeleteMLModel service method.

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.

Declared In

AWSMachineLearningService.h

– deleteRealtimeEndpoint:

Deletes a real time endpoint of an MLModel.

- (AWSTask<AWSMachineLearningDeleteRealtimeEndpointOutput*> *)deleteRealtimeEndpoint:(AWSMachineLearningDeleteRealtimeEndpointInput *)request

Parameters

request

A container for the necessary parameters to execute the DeleteRealtimeEndpoint service method.

Return Value

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.

Declared In

AWSMachineLearningService.h

– deleteRealtimeEndpoint:completionHandler:

Deletes a real time endpoint of an MLModel.

- (void)deleteRealtimeEndpoint:(AWSMachineLearningDeleteRealtimeEndpointInput *)request completionHandler:(void ( ^ _Nullable ) ( AWSMachineLearningDeleteRealtimeEndpointOutput *_Nullable response , NSError *_Nullable error ))completionHandler

Parameters

request

A container for the necessary parameters to execute the DeleteRealtimeEndpoint service method.

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.

Declared In

AWSMachineLearningService.h

– deleteTags:

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.

- (AWSTask<AWSMachineLearningDeleteTagsOutput*> *)deleteTags:(AWSMachineLearningDeleteTagsInput *)request

Parameters

request

A container for the necessary parameters to execute the DeleteTags service method.

Return Value

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.

Declared In

AWSMachineLearningService.h

– deleteTags: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.

- (void)deleteTags:(AWSMachineLearningDeleteTagsInput *)request completionHandler:(void ( ^ _Nullable ) ( AWSMachineLearningDeleteTagsOutput *_Nullable response , NSError *_Nullable error ))completionHandler

Parameters

request

A container for the necessary parameters to execute the DeleteTags service method.

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.

Declared In

AWSMachineLearningService.h

– describeBatchPredictions:

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

- (AWSTask<AWSMachineLearningDescribeBatchPredictionsOutput*> *)describeBatchPredictions:(AWSMachineLearningDescribeBatchPredictionsInput *)request

Parameters

request

A container for the necessary parameters to execute the DescribeBatchPredictions service method.

Return Value

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.

Declared In

AWSMachineLearningService.h

– describeBatchPredictions:completionHandler:

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

- (void)describeBatchPredictions:(AWSMachineLearningDescribeBatchPredictionsInput *)request completionHandler:(void ( ^ _Nullable ) ( AWSMachineLearningDescribeBatchPredictionsOutput *_Nullable response , NSError *_Nullable error ))completionHandler

Parameters

request

A container for the necessary parameters to execute the DescribeBatchPredictions service method.

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.

Declared In

AWSMachineLearningService.h

– describeDataSources:

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

- (AWSTask<AWSMachineLearningDescribeDataSourcesOutput*> *)describeDataSources:(AWSMachineLearningDescribeDataSourcesInput *)request

Parameters

request

A container for the necessary parameters to execute the DescribeDataSources service method.

Return Value

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.

Declared In

AWSMachineLearningService.h

– describeDataSources:completionHandler:

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

- (void)describeDataSources:(AWSMachineLearningDescribeDataSourcesInput *)request completionHandler:(void ( ^ _Nullable ) ( AWSMachineLearningDescribeDataSourcesOutput *_Nullable response , NSError *_Nullable error ))completionHandler

Parameters

request

A container for the necessary parameters to execute the DescribeDataSources service method.

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.

Declared In

AWSMachineLearningService.h

– describeEvaluations:

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

- (AWSTask<AWSMachineLearningDescribeEvaluationsOutput*> *)describeEvaluations:(AWSMachineLearningDescribeEvaluationsInput *)request

Parameters

request

A container for the necessary parameters to execute the DescribeEvaluations service method.

Return Value

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.

Declared In

AWSMachineLearningService.h

– describeEvaluations:completionHandler:

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

- (void)describeEvaluations:(AWSMachineLearningDescribeEvaluationsInput *)request completionHandler:(void ( ^ _Nullable ) ( AWSMachineLearningDescribeEvaluationsOutput *_Nullable response , NSError *_Nullable error ))completionHandler

Parameters

request

A container for the necessary parameters to execute the DescribeEvaluations service method.

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.

Declared In

AWSMachineLearningService.h

– describeMLModels:

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

- (AWSTask<AWSMachineLearningDescribeMLModelsOutput*> *)describeMLModels:(AWSMachineLearningDescribeMLModelsInput *)request

Parameters

request

A container for the necessary parameters to execute the DescribeMLModels service method.

Return Value

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.

Declared In

AWSMachineLearningService.h

– describeMLModels:completionHandler:

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

- (void)describeMLModels:(AWSMachineLearningDescribeMLModelsInput *)request completionHandler:(void ( ^ _Nullable ) ( AWSMachineLearningDescribeMLModelsOutput *_Nullable response , NSError *_Nullable error ))completionHandler

Parameters

request

A container for the necessary parameters to execute the DescribeMLModels service method.

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.

Declared In

AWSMachineLearningService.h

– describeTags:

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

- (AWSTask<AWSMachineLearningDescribeTagsOutput*> *)describeTags:(AWSMachineLearningDescribeTagsInput *)request

Parameters

request

A container for the necessary parameters to execute the DescribeTags service method.

Return Value

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.

Declared In

AWSMachineLearningService.h

– describeTags:completionHandler:

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

- (void)describeTags:(AWSMachineLearningDescribeTagsInput *)request completionHandler:(void ( ^ _Nullable ) ( AWSMachineLearningDescribeTagsOutput *_Nullable response , NSError *_Nullable error ))completionHandler

Parameters

request

A container for the necessary parameters to execute the DescribeTags service method.

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.

Declared In

AWSMachineLearningService.h

– getBatchPrediction:

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

- (AWSTask<AWSMachineLearningGetBatchPredictionOutput*> *)getBatchPrediction:(AWSMachineLearningGetBatchPredictionInput *)request

Parameters

request

A container for the necessary parameters to execute the GetBatchPrediction service method.

Return Value

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.

Declared In

AWSMachineLearningService.h

– getBatchPrediction:completionHandler:

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

- (void)getBatchPrediction:(AWSMachineLearningGetBatchPredictionInput *)request completionHandler:(void ( ^ _Nullable ) ( AWSMachineLearningGetBatchPredictionOutput *_Nullable response , NSError *_Nullable error ))completionHandler

Parameters

request

A container for the necessary parameters to execute the GetBatchPrediction service method.

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.

Declared In

AWSMachineLearningService.h

– getDataSource:

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.

- (AWSTask<AWSMachineLearningGetDataSourceOutput*> *)getDataSource:(AWSMachineLearningGetDataSourceInput *)request

Parameters

request

A container for the necessary parameters to execute the GetDataSource service method.

Return Value

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.

Declared In

AWSMachineLearningService.h

– getDataSource: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.

- (void)getDataSource:(AWSMachineLearningGetDataSourceInput *)request completionHandler:(void ( ^ _Nullable ) ( AWSMachineLearningGetDataSourceOutput *_Nullable response , NSError *_Nullable error ))completionHandler

Parameters

request

A container for the necessary parameters to execute the GetDataSource service method.

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.

Declared In

AWSMachineLearningService.h

– getEvaluation:

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

- (AWSTask<AWSMachineLearningGetEvaluationOutput*> *)getEvaluation:(AWSMachineLearningGetEvaluationInput *)request

Parameters

request

A container for the necessary parameters to execute the GetEvaluation service method.

Return Value

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.

Declared In

AWSMachineLearningService.h

– getEvaluation:completionHandler:

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

- (void)getEvaluation:(AWSMachineLearningGetEvaluationInput *)request completionHandler:(void ( ^ _Nullable ) ( AWSMachineLearningGetEvaluationOutput *_Nullable response , NSError *_Nullable error ))completionHandler

Parameters

request

A container for the necessary parameters to execute the GetEvaluation service method.

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.

Declared In

AWSMachineLearningService.h

– getMLModel:

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.

- (AWSTask<AWSMachineLearningGetMLModelOutput*> *)getMLModel:(AWSMachineLearningGetMLModelInput *)request

Parameters

request

A container for the necessary parameters to execute the GetMLModel service method.

Return Value

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.

Declared In

AWSMachineLearningService.h

– getMLModel: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.

- (void)getMLModel:(AWSMachineLearningGetMLModelInput *)request completionHandler:(void ( ^ _Nullable ) ( AWSMachineLearningGetMLModelOutput *_Nullable response , NSError *_Nullable error ))completionHandler

Parameters

request

A container for the necessary parameters to execute the GetMLModel service method.

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.

Declared In

AWSMachineLearningService.h

– predict:

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.

- (AWSTask<AWSMachineLearningPredictOutput*> *)predict:(AWSMachineLearningPredictInput *)request

Parameters

request

A container for the necessary parameters to execute the Predict service method.

Return Value

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.

Declared In

AWSMachineLearningService.h

– predict: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.

- (void)predict:(AWSMachineLearningPredictInput *)request completionHandler:(void ( ^ _Nullable ) ( AWSMachineLearningPredictOutput *_Nullable response , NSError *_Nullable error ))completionHandler

Parameters

request

A container for the necessary parameters to execute the Predict service method.

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.

Declared In

AWSMachineLearningService.h

– updateBatchPrediction:

Updates the BatchPredictionName of a BatchPrediction.

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

- (AWSTask<AWSMachineLearningUpdateBatchPredictionOutput*> *)updateBatchPrediction:(AWSMachineLearningUpdateBatchPredictionInput *)request

Parameters

request

A container for the necessary parameters to execute the UpdateBatchPrediction service method.

Return Value

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.

Declared In

AWSMachineLearningService.h

– updateBatchPrediction:completionHandler:

Updates the BatchPredictionName of a BatchPrediction.

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

- (void)updateBatchPrediction:(AWSMachineLearningUpdateBatchPredictionInput *)request completionHandler:(void ( ^ _Nullable ) ( AWSMachineLearningUpdateBatchPredictionOutput *_Nullable response , NSError *_Nullable error ))completionHandler

Parameters

request

A container for the necessary parameters to execute the UpdateBatchPrediction service method.

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.

Declared In

AWSMachineLearningService.h

– updateDataSource:

Updates the DataSourceName of a DataSource.

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

- (AWSTask<AWSMachineLearningUpdateDataSourceOutput*> *)updateDataSource:(AWSMachineLearningUpdateDataSourceInput *)request

Parameters

request

A container for the necessary parameters to execute the UpdateDataSource service method.

Return Value

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.

Declared In

AWSMachineLearningService.h

– updateDataSource:completionHandler:

Updates the DataSourceName of a DataSource.

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

- (void)updateDataSource:(AWSMachineLearningUpdateDataSourceInput *)request completionHandler:(void ( ^ _Nullable ) ( AWSMachineLearningUpdateDataSourceOutput *_Nullable response , NSError *_Nullable error ))completionHandler

Parameters

request

A container for the necessary parameters to execute the UpdateDataSource service method.

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.

Declared In

AWSMachineLearningService.h

– updateEvaluation:

Updates the EvaluationName of an Evaluation.

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

- (AWSTask<AWSMachineLearningUpdateEvaluationOutput*> *)updateEvaluation:(AWSMachineLearningUpdateEvaluationInput *)request

Parameters

request

A container for the necessary parameters to execute the UpdateEvaluation service method.

Return Value

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.

Declared In

AWSMachineLearningService.h

– updateEvaluation:completionHandler:

Updates the EvaluationName of an Evaluation.

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

- (void)updateEvaluation:(AWSMachineLearningUpdateEvaluationInput *)request completionHandler:(void ( ^ _Nullable ) ( AWSMachineLearningUpdateEvaluationOutput *_Nullable response , NSError *_Nullable error ))completionHandler

Parameters

request

A container for the necessary parameters to execute the UpdateEvaluation service method.

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.

Declared In

AWSMachineLearningService.h

– updateMLModel:

Updates the MLModelName and the ScoreThreshold of an MLModel.

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

- (AWSTask<AWSMachineLearningUpdateMLModelOutput*> *)updateMLModel:(AWSMachineLearningUpdateMLModelInput *)request

Parameters

request

A container for the necessary parameters to execute the UpdateMLModel service method.

Return Value

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.

Declared In

AWSMachineLearningService.h

– updateMLModel: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.

- (void)updateMLModel:(AWSMachineLearningUpdateMLModelInput *)request completionHandler:(void ( ^ _Nullable ) ( AWSMachineLearningUpdateMLModelOutput *_Nullable response , NSError *_Nullable error ))completionHandler

Parameters

request

A container for the necessary parameters to execute the UpdateMLModel service method.

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.

Declared In

AWSMachineLearningService.h