AWSMachineLearning

@interface AWSMachineLearning

Definition of the public APIs exposed by Amazon Machine Learning

  • 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.

    Declaration

    Objective-C

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

    Swift

    var configuration: UnsafeMutablePointer<Int32> { get }
  • 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];
    

    Declaration

    Objective-C

    + (nonnull instancetype)defaultMachineLearning;

    Swift

    class func `default`() -> Self

    Return Value

    The default service client.

  • 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.

    Declaration

    Objective-C

    + (void)registerMachineLearningWithConfiguration:(id)configuration
                                              forKey:(nonnull NSString *)key;

    Swift

    class func register(withConfiguration configuration: Any!, forKey key: String)

    Parameters

    configuration

    A service configuration object.

    key

    A string to identify the service client.

  • 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"];
    

    Declaration

    Objective-C

    + (nonnull instancetype)MachineLearningForKey:(nonnull NSString *)key;

    Swift

    convenience init(forKey key: String)

    Parameters

    key

    A string to identify the service client.

    Return Value

    An instance of the service client.

  • 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.

    Declaration

    Objective-C

    + (void)removeMachineLearningForKey:(nonnull NSString *)key;

    Swift

    class func remove(forKey key: String)

    Parameters

    key

    A string to identify the service client.

  • Adds one or more tags to an object, up to a limit of 10. Each tag consists of a key and an optional value. If you add a tag using a key that is already associated with the ML object, AddTags updates the tag’s value.

    See

    AWSMachineLearningAddTagsInput

    See

    AWSMachineLearningAddTagsOutput

    Declaration

    Objective-C

    - (id)addTags:(nonnull AWSMachineLearningAddTagsInput *)request;

    Swift

    func addTags(_ request: AWSMachineLearningAddTagsInput) -> Any!

    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.

  • Adds one or more tags to an object, up to a limit of 10. Each tag consists of a key and an optional value. If you add a tag using a key that is already associated with the ML object, AddTags updates the tag’s value.

    See

    AWSMachineLearningAddTagsInput

    See

    AWSMachineLearningAddTagsOutput

    Declaration

    Objective-C

    - (void)addTags:(nonnull AWSMachineLearningAddTagsInput *)request
        completionHandler:
            (void (^_Nullable)(AWSMachineLearningAddTagsOutput *_Nullable,
                               NSError *_Nullable))completionHandler;

    Swift

    func addTags(_ request: AWSMachineLearningAddTagsInput, completionHandler: ((AWSMachineLearningAddTagsOutput?, Error?) -> Void)? = nil)

    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.

  • Generates predictions for a group of observations. The observations to process exist in one or more data files referenced by a DataSource. This operation creates a new BatchPrediction, and uses an MLModel and the data files referenced by the DataSource as information sources.

    CreateBatchPrediction is an asynchronous operation. In response to CreateBatchPrediction, Amazon Machine Learning (Amazon ML) immediately returns and sets the BatchPrediction status to PENDING. After the BatchPrediction completes, Amazon ML sets the status to COMPLETED.

    You can poll for status updates by using the GetBatchPrediction operation and checking the Status parameter of the result. After the COMPLETED status appears, the results are available in the location specified by the OutputUri parameter.

    See

    AWSMachineLearningCreateBatchPredictionInput

    See

    AWSMachineLearningCreateBatchPredictionOutput

    Declaration

    Objective-C

    - (id)createBatchPrediction:
        (nonnull AWSMachineLearningCreateBatchPredictionInput *)request;

    Swift

    func createBatchPrediction(_ request: AWSMachineLearningCreateBatchPredictionInput) -> Any!

    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.

  • Generates predictions for a group of observations. The observations to process exist in one or more data files referenced by a DataSource. This operation creates a new BatchPrediction, and uses an MLModel and the data files referenced by the DataSource as information sources.

    CreateBatchPrediction is an asynchronous operation. In response to CreateBatchPrediction, Amazon Machine Learning (Amazon ML) immediately returns and sets the BatchPrediction status to PENDING. After the BatchPrediction completes, Amazon ML sets the status to COMPLETED.

    You can poll for status updates by using the GetBatchPrediction operation and checking the Status parameter of the result. After the COMPLETED status appears, the results are available in the location specified by the OutputUri parameter.

    See

    AWSMachineLearningCreateBatchPredictionInput

    See

    AWSMachineLearningCreateBatchPredictionOutput

    Declaration

    Objective-C

    - (void)createBatchPrediction:
                (nonnull AWSMachineLearningCreateBatchPredictionInput *)request
                completionHandler:
                    (void (^_Nullable)(
                        AWSMachineLearningCreateBatchPredictionOutput *_Nullable,
                        NSError *_Nullable))completionHandler;

    Swift

    func createBatchPrediction(_ request: AWSMachineLearningCreateBatchPredictionInput, completionHandler: ((AWSMachineLearningCreateBatchPredictionOutput?, Error?) -> Void)? = nil)

    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.

  • Creates a DataSource object from an Amazon Relational Database Service (Amazon RDS). A DataSource references data that can be used to perform CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations.

    CreateDataSourceFromRDS is an asynchronous operation. In response to CreateDataSourceFromRDS, Amazon Machine Learning (Amazon ML) immediately returns and sets the DataSource status to PENDING. After the DataSource is created and ready for use, Amazon ML sets the Status parameter to COMPLETED. DataSource in the COMPLETED or PENDING state can be used only to perform >CreateMLModel>, CreateEvaluation, or CreateBatchPrediction operations.

    If Amazon ML cannot accept the input source, it sets the Status parameter to FAILED and includes an error message in the Message attribute of the GetDataSource operation response.

    See

    AWSMachineLearningCreateDataSourceFromRDSInput

    See

    AWSMachineLearningCreateDataSourceFromRDSOutput

    Declaration

    Objective-C

    - (id)createDataSourceFromRDS:
        (nonnull AWSMachineLearningCreateDataSourceFromRDSInput *)request;

    Swift

    func createDataSource(fromRDS request: AWSMachineLearningCreateDataSourceFromRDSInput) -> Any!

    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.

  • Creates a DataSource object from an Amazon Relational Database Service (Amazon RDS). A DataSource references data that can be used to perform CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations.

    CreateDataSourceFromRDS is an asynchronous operation. In response to CreateDataSourceFromRDS, Amazon Machine Learning (Amazon ML) immediately returns and sets the DataSource status to PENDING. After the DataSource is created and ready for use, Amazon ML sets the Status parameter to COMPLETED. DataSource in the COMPLETED or PENDING state can be used only to perform >CreateMLModel>, CreateEvaluation, or CreateBatchPrediction operations.

    If Amazon ML cannot accept the input source, it sets the Status parameter to FAILED and includes an error message in the Message attribute of the GetDataSource operation response.

    See

    AWSMachineLearningCreateDataSourceFromRDSInput

    See

    AWSMachineLearningCreateDataSourceFromRDSOutput

    Declaration

    Objective-C

    - (void)
        createDataSourceFromRDS:
            (nonnull AWSMachineLearningCreateDataSourceFromRDSInput *)request
              completionHandler:
                  (void (^_Nullable)(
                      AWSMachineLearningCreateDataSourceFromRDSOutput *_Nullable,
                      NSError *_Nullable))completionHandler;

    Swift

    func createDataSource(fromRDS request: AWSMachineLearningCreateDataSourceFromRDSInput, completionHandler: ((AWSMachineLearningCreateDataSourceFromRDSOutput?, Error?) -> Void)? = nil)

    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.

  • 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>

    See

    AWSMachineLearningCreateDataSourceFromRedshiftInput

    See

    AWSMachineLearningCreateDataSourceFromRedshiftOutput

    Declaration

    Objective-C

    - (id)createDataSourceFromRedshift:
        (nonnull AWSMachineLearningCreateDataSourceFromRedshiftInput *)request;

    Swift

    func createDataSource(fromRedshift request: AWSMachineLearningCreateDataSourceFromRedshiftInput) -> Any!

    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.

  • 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>

    See

    AWSMachineLearningCreateDataSourceFromRedshiftInput

    See

    AWSMachineLearningCreateDataSourceFromRedshiftOutput

    Declaration

    Objective-C

    - (void)createDataSourceFromRedshift:
                (nonnull AWSMachineLearningCreateDataSourceFromRedshiftInput *)
                    request
                       completionHandler:
                           (void (^_Nullable)(
                               AWSMachineLearningCreateDataSourceFromRedshiftOutput
                                   *_Nullable,
                               NSError *_Nullable))completionHandler;

    Swift

    func createDataSource(fromRedshift request: AWSMachineLearningCreateDataSourceFromRedshiftInput, completionHandler: ((AWSMachineLearningCreateDataSourceFromRedshiftOutput?, Error?) -> Void)? = nil)

    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.

  • Creates a DataSource object. A DataSource references data that can be used to perform CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations.

    CreateDataSourceFromS3 is an asynchronous operation. In response to CreateDataSourceFromS3, Amazon Machine Learning (Amazon ML) immediately returns and sets the DataSource status to PENDING. After the DataSource has been created and is ready for use, Amazon ML sets the Status parameter to COMPLETED. DataSource in the COMPLETED or PENDING state can be used to perform only CreateMLModel, CreateEvaluation or CreateBatchPrediction operations.

    If Amazon ML can’t accept the input source, it sets the Status parameter to FAILED and includes an error message in the Message attribute of the GetDataSource operation response.

    The observation data used in a DataSource should be ready to use; that is, it should have a consistent structure, and missing data values should be kept to a minimum. The observation data must reside in one or more .csv files in an Amazon Simple Storage Service (Amazon S3) location, along with a schema that describes the data items by name and type. The same schema must be used for all of the data files referenced by the DataSource.

    After the DataSource has been created, it’s ready to use in evaluations and batch predictions. If you plan to use the DataSource to train an MLModel, the DataSource also needs a recipe. A recipe describes how each input variable will be used in training an MLModel. Will the variable be included or excluded from training? Will the variable be manipulated; for example, will it be combined with another variable or will it be split apart into word combinations? The recipe provides answers to these questions.

    See

    AWSMachineLearningCreateDataSourceFromS3Input

    See

    AWSMachineLearningCreateDataSourceFromS3Output

    Declaration

    Objective-C

    - (id)createDataSourceFromS3:
        (nonnull AWSMachineLearningCreateDataSourceFromS3Input *)request;

    Swift

    func createDataSource(fromS3 request: AWSMachineLearningCreateDataSourceFromS3Input) -> Any!

    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.

  • Creates a DataSource object. A DataSource references data that can be used to perform CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations.

    CreateDataSourceFromS3 is an asynchronous operation. In response to CreateDataSourceFromS3, Amazon Machine Learning (Amazon ML) immediately returns and sets the DataSource status to PENDING. After the DataSource has been created and is ready for use, Amazon ML sets the Status parameter to COMPLETED. DataSource in the COMPLETED or PENDING state can be used to perform only CreateMLModel, CreateEvaluation or CreateBatchPrediction operations.

    If Amazon ML can’t accept the input source, it sets the Status parameter to FAILED and includes an error message in the Message attribute of the GetDataSource operation response.

    The observation data used in a DataSource should be ready to use; that is, it should have a consistent structure, and missing data values should be kept to a minimum. The observation data must reside in one or more .csv files in an Amazon Simple Storage Service (Amazon S3) location, along with a schema that describes the data items by name and type. The same schema must be used for all of the data files referenced by the DataSource.

    After the DataSource has been created, it’s ready to use in evaluations and batch predictions. If you plan to use the DataSource to train an MLModel, the DataSource also needs a recipe. A recipe describes how each input variable will be used in training an MLModel. Will the variable be included or excluded from training? Will the variable be manipulated; for example, will it be combined with another variable or will it be split apart into word combinations? The recipe provides answers to these questions.

    See

    AWSMachineLearningCreateDataSourceFromS3Input

    See

    AWSMachineLearningCreateDataSourceFromS3Output

    Declaration

    Objective-C

    - (void)createDataSourceFromS3:
                (nonnull AWSMachineLearningCreateDataSourceFromS3Input *)request
                 completionHandler:
                     (void (^_Nullable)(
                         AWSMachineLearningCreateDataSourceFromS3Output *_Nullable,
                         NSError *_Nullable))completionHandler;

    Swift

    func createDataSource(fromS3 request: AWSMachineLearningCreateDataSourceFromS3Input, completionHandler: ((AWSMachineLearningCreateDataSourceFromS3Output?, Error?) -> Void)? = nil)

    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.

  • Creates a new Evaluation of an MLModel. An MLModel is evaluated on a set of observations associated to a DataSource. Like a DataSource for an MLModel, the DataSource for an Evaluation contains values for the Target Variable. The Evaluation compares the predicted result for each observation to the actual outcome and provides a summary so that you know how effective the MLModel functions on the test data. Evaluation generates a relevant performance metric, such as BinaryAUC, RegressionRMSE or MulticlassAvgFScore based on the corresponding MLModelType: BINARY, REGRESSION or MULTICLASS.

    CreateEvaluation is an asynchronous operation. In response to CreateEvaluation, Amazon Machine Learning (Amazon ML) immediately returns and sets the evaluation status to PENDING. After the Evaluation is created and ready for use, Amazon ML sets the status to COMPLETED.

    You can use the GetEvaluation operation to check progress of the evaluation during the creation operation.

    See

    AWSMachineLearningCreateEvaluationInput

    See

    AWSMachineLearningCreateEvaluationOutput

    Declaration

    Objective-C

    - (id)createEvaluation:
        (nonnull AWSMachineLearningCreateEvaluationInput *)request;

    Swift

    func createEvaluation(_ request: AWSMachineLearningCreateEvaluationInput) -> Any!

    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.

  • Creates a new Evaluation of an MLModel. An MLModel is evaluated on a set of observations associated to a DataSource. Like a DataSource for an MLModel, the DataSource for an Evaluation contains values for the Target Variable. The Evaluation compares the predicted result for each observation to the actual outcome and provides a summary so that you know how effective the MLModel functions on the test data. Evaluation generates a relevant performance metric, such as BinaryAUC, RegressionRMSE or MulticlassAvgFScore based on the corresponding MLModelType: BINARY, REGRESSION or MULTICLASS.

    CreateEvaluation is an asynchronous operation. In response to CreateEvaluation, Amazon Machine Learning (Amazon ML) immediately returns and sets the evaluation status to PENDING. After the Evaluation is created and ready for use, Amazon ML sets the status to COMPLETED.

    You can use the GetEvaluation operation to check progress of the evaluation during the creation operation.

    See

    AWSMachineLearningCreateEvaluationInput

    See

    AWSMachineLearningCreateEvaluationOutput

    Declaration

    Objective-C

    - (void)
         createEvaluation:(nonnull AWSMachineLearningCreateEvaluationInput *)request
        completionHandler:
            (void (^_Nullable)(AWSMachineLearningCreateEvaluationOutput *_Nullable,
                               NSError *_Nullable))completionHandler;

    Swift

    func createEvaluation(_ request: AWSMachineLearningCreateEvaluationInput, completionHandler: ((AWSMachineLearningCreateEvaluationOutput?, Error?) -> Void)? = nil)

    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.

  • Creates a new MLModel using the DataSource and the recipe as information sources.

    An MLModel is nearly immutable. Users can update only the MLModelName and the ScoreThreshold in an MLModel without creating a new MLModel.

    CreateMLModel is an asynchronous operation. In response to CreateMLModel, Amazon Machine Learning (Amazon ML) immediately returns and sets the MLModel status to PENDING. After the MLModel has been created and ready is for use, Amazon ML sets the status to COMPLETED.

    You can use the GetMLModel operation to check the progress of the MLModel during the creation operation.

    CreateMLModel requires a DataSource with computed statistics, which can be created by setting ComputeStatistics to true in CreateDataSourceFromRDS, CreateDataSourceFromS3, or CreateDataSourceFromRedshift operations.

    See

    AWSMachineLearningCreateMLModelInput

    See

    AWSMachineLearningCreateMLModelOutput

    Declaration

    Objective-C

    - (id)createMLModel:(nonnull AWSMachineLearningCreateMLModelInput *)request;

    Swift

    func createMLModel(_ request: AWSMachineLearningCreateMLModelInput) -> Any!

    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.

  • Creates a new MLModel using the DataSource and the recipe as information sources.

    An MLModel is nearly immutable. Users can update only the MLModelName and the ScoreThreshold in an MLModel without creating a new MLModel.

    CreateMLModel is an asynchronous operation. In response to CreateMLModel, Amazon Machine Learning (Amazon ML) immediately returns and sets the MLModel status to PENDING. After the MLModel has been created and ready is for use, Amazon ML sets the status to COMPLETED.

    You can use the GetMLModel operation to check the progress of the MLModel during the creation operation.

    CreateMLModel requires a DataSource with computed statistics, which can be created by setting ComputeStatistics to true in CreateDataSourceFromRDS, CreateDataSourceFromS3, or CreateDataSourceFromRedshift operations.

    See

    AWSMachineLearningCreateMLModelInput

    See

    AWSMachineLearningCreateMLModelOutput

    Declaration

    Objective-C

    - (void)createMLModel:(nonnull AWSMachineLearningCreateMLModelInput *)request
        completionHandler:
            (void (^_Nullable)(AWSMachineLearningCreateMLModelOutput *_Nullable,
                               NSError *_Nullable))completionHandler;

    Swift

    func createMLModel(_ request: AWSMachineLearningCreateMLModelInput, completionHandler: ((AWSMachineLearningCreateMLModelOutput?, Error?) -> Void)? = nil)

    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.

  • Creates a real-time endpoint for the MLModel. The endpoint contains the URI of the MLModel; that is, the location to send real-time prediction requests for the specified MLModel.

    See

    AWSMachineLearningCreateRealtimeEndpointInput

    See

    AWSMachineLearningCreateRealtimeEndpointOutput

    Declaration

    Objective-C

    - (id)createRealtimeEndpoint:
        (nonnull AWSMachineLearningCreateRealtimeEndpointInput *)request;

    Swift

    func createRealtimeEndpoint(_ request: AWSMachineLearningCreateRealtimeEndpointInput) -> Any!

    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.

  • Creates a real-time endpoint for the MLModel. The endpoint contains the URI of the MLModel; that is, the location to send real-time prediction requests for the specified MLModel.

    See

    AWSMachineLearningCreateRealtimeEndpointInput

    See

    AWSMachineLearningCreateRealtimeEndpointOutput

    Declaration

    Objective-C

    - (void)createRealtimeEndpoint:
                (nonnull AWSMachineLearningCreateRealtimeEndpointInput *)request
                 completionHandler:
                     (void (^_Nullable)(
                         AWSMachineLearningCreateRealtimeEndpointOutput *_Nullable,
                         NSError *_Nullable))completionHandler;

    Swift

    func createRealtimeEndpoint(_ request: AWSMachineLearningCreateRealtimeEndpointInput, completionHandler: ((AWSMachineLearningCreateRealtimeEndpointOutput?, Error?) -> Void)? = nil)

    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.

  • Assigns the DELETED status to a BatchPrediction, rendering it unusable.

    After using the DeleteBatchPrediction operation, you can use the GetBatchPrediction operation to verify that the status of the BatchPrediction changed to DELETED.

    Caution: The result of the DeleteBatchPrediction operation is irreversible.

    See

    AWSMachineLearningDeleteBatchPredictionInput

    See

    AWSMachineLearningDeleteBatchPredictionOutput

    Declaration

    Objective-C

    - (id)deleteBatchPrediction:
        (nonnull AWSMachineLearningDeleteBatchPredictionInput *)request;

    Swift

    func deleteBatchPrediction(_ request: AWSMachineLearningDeleteBatchPredictionInput) -> Any!

    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.

  • Assigns the DELETED status to a BatchPrediction, rendering it unusable.

    After using the DeleteBatchPrediction operation, you can use the GetBatchPrediction operation to verify that the status of the BatchPrediction changed to DELETED.

    Caution: The result of the DeleteBatchPrediction operation is irreversible.

    See

    AWSMachineLearningDeleteBatchPredictionInput

    See

    AWSMachineLearningDeleteBatchPredictionOutput

    Declaration

    Objective-C

    - (void)deleteBatchPrediction:
                (nonnull AWSMachineLearningDeleteBatchPredictionInput *)request
                completionHandler:
                    (void (^_Nullable)(
                        AWSMachineLearningDeleteBatchPredictionOutput *_Nullable,
                        NSError *_Nullable))completionHandler;

    Swift

    func deleteBatchPrediction(_ request: AWSMachineLearningDeleteBatchPredictionInput, completionHandler: ((AWSMachineLearningDeleteBatchPredictionOutput?, Error?) -> Void)? = nil)

    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.

  • Assigns the DELETED status to a DataSource, rendering it unusable.

    After using the DeleteDataSource operation, you can use the GetDataSource operation to verify that the status of the DataSource changed to DELETED.

    Caution: The results of the DeleteDataSource operation are irreversible.

    See

    AWSMachineLearningDeleteDataSourceInput

    See

    AWSMachineLearningDeleteDataSourceOutput

    Declaration

    Objective-C

    - (id)deleteDataSource:
        (nonnull AWSMachineLearningDeleteDataSourceInput *)request;

    Swift

    func deleteDataSource(_ request: AWSMachineLearningDeleteDataSourceInput) -> Any!

    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.

  • Assigns the DELETED status to a DataSource, rendering it unusable.

    After using the DeleteDataSource operation, you can use the GetDataSource operation to verify that the status of the DataSource changed to DELETED.

    Caution: The results of the DeleteDataSource operation are irreversible.

    See

    AWSMachineLearningDeleteDataSourceInput

    See

    AWSMachineLearningDeleteDataSourceOutput

    Declaration

    Objective-C

    - (void)
         deleteDataSource:(nonnull AWSMachineLearningDeleteDataSourceInput *)request
        completionHandler:
            (void (^_Nullable)(AWSMachineLearningDeleteDataSourceOutput *_Nullable,
                               NSError *_Nullable))completionHandler;

    Swift

    func deleteDataSource(_ request: AWSMachineLearningDeleteDataSourceInput, completionHandler: ((AWSMachineLearningDeleteDataSourceOutput?, Error?) -> Void)? = nil)

    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.

  • Assigns the DELETED status to an Evaluation, rendering it unusable.

    After invoking the DeleteEvaluation operation, you can use the GetEvaluation operation to verify that the status of the Evaluation changed to DELETED.

    Caution

    The results of the DeleteEvaluation operation are irreversible.

    See

    AWSMachineLearningDeleteEvaluationInput

    See

    AWSMachineLearningDeleteEvaluationOutput

    Declaration

    Objective-C

    - (id)deleteEvaluation:
        (nonnull AWSMachineLearningDeleteEvaluationInput *)request;

    Swift

    func deleteEvaluation(_ request: AWSMachineLearningDeleteEvaluationInput) -> Any!

    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.

  • Assigns the DELETED status to an Evaluation, rendering it unusable.

    After invoking the DeleteEvaluation operation, you can use the GetEvaluation operation to verify that the status of the Evaluation changed to DELETED.

    Caution

    The results of the DeleteEvaluation operation are irreversible.

    See

    AWSMachineLearningDeleteEvaluationInput

    See

    AWSMachineLearningDeleteEvaluationOutput

    Declaration

    Objective-C

    - (void)
         deleteEvaluation:(nonnull AWSMachineLearningDeleteEvaluationInput *)request
        completionHandler:
            (void (^_Nullable)(AWSMachineLearningDeleteEvaluationOutput *_Nullable,
                               NSError *_Nullable))completionHandler;

    Swift

    func deleteEvaluation(_ request: AWSMachineLearningDeleteEvaluationInput, completionHandler: ((AWSMachineLearningDeleteEvaluationOutput?, Error?) -> Void)? = nil)

    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.

  • Assigns the DELETED status to an MLModel, rendering it unusable.

    After using the DeleteMLModel operation, you can use the GetMLModel operation to verify that the status of the MLModel changed to DELETED.

    Caution: The result of the DeleteMLModel operation is irreversible.

    See

    AWSMachineLearningDeleteMLModelInput

    See

    AWSMachineLearningDeleteMLModelOutput

    Declaration

    Objective-C

    - (id)deleteMLModel:(nonnull AWSMachineLearningDeleteMLModelInput *)request;

    Swift

    func deleteMLModel(_ request: AWSMachineLearningDeleteMLModelInput) -> Any!

    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.

  • Assigns the DELETED status to an MLModel, rendering it unusable.

    After using the DeleteMLModel operation, you can use the GetMLModel operation to verify that the status of the MLModel changed to DELETED.

    Caution: The result of the DeleteMLModel operation is irreversible.

    See

    AWSMachineLearningDeleteMLModelInput

    See

    AWSMachineLearningDeleteMLModelOutput

    Declaration

    Objective-C

    - (void)deleteMLModel:(nonnull AWSMachineLearningDeleteMLModelInput *)request
        completionHandler:
            (void (^_Nullable)(AWSMachineLearningDeleteMLModelOutput *_Nullable,
                               NSError *_Nullable))completionHandler;

    Swift

    func deleteMLModel(_ request: AWSMachineLearningDeleteMLModelInput, completionHandler: ((AWSMachineLearningDeleteMLModelOutput?, Error?) -> Void)? = nil)

    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.

  • Deletes a real time endpoint of an MLModel.

    See

    AWSMachineLearningDeleteRealtimeEndpointInput

    See

    AWSMachineLearningDeleteRealtimeEndpointOutput

    Declaration

    Objective-C

    - (id)deleteRealtimeEndpoint:
        (nonnull AWSMachineLearningDeleteRealtimeEndpointInput *)request;

    Swift

    func deleteRealtimeEndpoint(_ request: AWSMachineLearningDeleteRealtimeEndpointInput) -> Any!

    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.

  • Deletes a real time endpoint of an MLModel.

    See

    AWSMachineLearningDeleteRealtimeEndpointInput

    See

    AWSMachineLearningDeleteRealtimeEndpointOutput

    Declaration

    Objective-C

    - (void)deleteRealtimeEndpoint:
                (nonnull AWSMachineLearningDeleteRealtimeEndpointInput *)request
                 completionHandler:
                     (void (^_Nullable)(
                         AWSMachineLearningDeleteRealtimeEndpointOutput *_Nullable,
                         NSError *_Nullable))completionHandler;

    Swift

    func deleteRealtimeEndpoint(_ request: AWSMachineLearningDeleteRealtimeEndpointInput, completionHandler: ((AWSMachineLearningDeleteRealtimeEndpointOutput?, Error?) -> Void)? = nil)

    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.

  • Deletes the specified tags associated with an ML object. After this operation is complete, you can’t recover deleted tags.

    If you specify a tag that doesn’t exist, Amazon ML ignores it.

    See

    AWSMachineLearningDeleteTagsInput

    See

    AWSMachineLearningDeleteTagsOutput

    Declaration

    Objective-C

    - (id)deleteTags:(nonnull AWSMachineLearningDeleteTagsInput *)request;

    Swift

    func deleteTags(_ request: AWSMachineLearningDeleteTagsInput) -> Any!

    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.

  • Deletes the specified tags associated with an ML object. After this operation is complete, you can’t recover deleted tags.

    If you specify a tag that doesn’t exist, Amazon ML ignores it.

    See

    AWSMachineLearningDeleteTagsInput

    See

    AWSMachineLearningDeleteTagsOutput

    Declaration

    Objective-C

    - (void)deleteTags:(nonnull AWSMachineLearningDeleteTagsInput *)request
        completionHandler:
            (void (^_Nullable)(AWSMachineLearningDeleteTagsOutput *_Nullable,
                               NSError *_Nullable))completionHandler;

    Swift

    func deleteTags(_ request: AWSMachineLearningDeleteTagsInput, completionHandler: ((AWSMachineLearningDeleteTagsOutput?, Error?) -> Void)? = nil)

    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.

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

    See

    AWSMachineLearningDescribeBatchPredictionsInput

    See

    AWSMachineLearningDescribeBatchPredictionsOutput

    Declaration

    Objective-C

    - (id)describeBatchPredictions:
        (nonnull AWSMachineLearningDescribeBatchPredictionsInput *)request;

    Swift

    func describeBatchPredictions(_ request: AWSMachineLearningDescribeBatchPredictionsInput) -> Any!

    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.

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

    See

    AWSMachineLearningDescribeBatchPredictionsInput

    See

    AWSMachineLearningDescribeBatchPredictionsOutput

    Declaration

    Objective-C

    - (void)
        describeBatchPredictions:
            (nonnull AWSMachineLearningDescribeBatchPredictionsInput *)request
               completionHandler:
                   (void (^_Nullable)(
                       AWSMachineLearningDescribeBatchPredictionsOutput *_Nullable,
                       NSError *_Nullable))completionHandler;

    Swift

    func describeBatchPredictions(_ request: AWSMachineLearningDescribeBatchPredictionsInput, completionHandler: ((AWSMachineLearningDescribeBatchPredictionsOutput?, Error?) -> Void)? = nil)

    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.

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

    See

    AWSMachineLearningDescribeDataSourcesInput

    See

    AWSMachineLearningDescribeDataSourcesOutput

    Declaration

    Objective-C

    - (id)describeDataSources:
        (nonnull AWSMachineLearningDescribeDataSourcesInput *)request;

    Swift

    func describeDataSources(_ request: AWSMachineLearningDescribeDataSourcesInput) -> Any!

    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.

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

    See

    AWSMachineLearningDescribeDataSourcesInput

    See

    AWSMachineLearningDescribeDataSourcesOutput

    Declaration

    Objective-C

    - (void)describeDataSources:
                (nonnull AWSMachineLearningDescribeDataSourcesInput *)request
              completionHandler:
                  (void (^_Nullable)(
                      AWSMachineLearningDescribeDataSourcesOutput *_Nullable,
                      NSError *_Nullable))completionHandler;

    Swift

    func describeDataSources(_ request: AWSMachineLearningDescribeDataSourcesInput, completionHandler: ((AWSMachineLearningDescribeDataSourcesOutput?, Error?) -> Void)? = nil)

    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.

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

    See

    AWSMachineLearningDescribeEvaluationsInput

    See

    AWSMachineLearningDescribeEvaluationsOutput

    Declaration

    Objective-C

    - (id)describeEvaluations:
        (nonnull AWSMachineLearningDescribeEvaluationsInput *)request;

    Swift

    func describeEvaluations(_ request: AWSMachineLearningDescribeEvaluationsInput) -> Any!

    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.

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

    See

    AWSMachineLearningDescribeEvaluationsInput

    See

    AWSMachineLearningDescribeEvaluationsOutput

    Declaration

    Objective-C

    - (void)describeEvaluations:
                (nonnull AWSMachineLearningDescribeEvaluationsInput *)request
              completionHandler:
                  (void (^_Nullable)(
                      AWSMachineLearningDescribeEvaluationsOutput *_Nullable,
                      NSError *_Nullable))completionHandler;

    Swift

    func describeEvaluations(_ request: AWSMachineLearningDescribeEvaluationsInput, completionHandler: ((AWSMachineLearningDescribeEvaluationsOutput?, Error?) -> Void)? = nil)

    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.

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

    See

    AWSMachineLearningDescribeMLModelsInput

    See

    AWSMachineLearningDescribeMLModelsOutput

    Declaration

    Objective-C

    - (id)describeMLModels:
        (nonnull AWSMachineLearningDescribeMLModelsInput *)request;

    Swift

    func describeMLModels(_ request: AWSMachineLearningDescribeMLModelsInput) -> Any!

    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.

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

    See

    AWSMachineLearningDescribeMLModelsInput

    See

    AWSMachineLearningDescribeMLModelsOutput

    Declaration

    Objective-C

    - (void)
         describeMLModels:(nonnull AWSMachineLearningDescribeMLModelsInput *)request
        completionHandler:
            (void (^_Nullable)(AWSMachineLearningDescribeMLModelsOutput *_Nullable,
                               NSError *_Nullable))completionHandler;

    Swift

    func describeMLModels(_ request: AWSMachineLearningDescribeMLModelsInput, completionHandler: ((AWSMachineLearningDescribeMLModelsOutput?, Error?) -> Void)? = nil)

    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.

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

    See

    AWSMachineLearningDescribeTagsInput

    See

    AWSMachineLearningDescribeTagsOutput

    Declaration

    Objective-C

    - (id)describeTags:(nonnull AWSMachineLearningDescribeTagsInput *)request;

    Swift

    func describeTags(_ request: AWSMachineLearningDescribeTagsInput) -> Any!

    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.

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

    See

    AWSMachineLearningDescribeTagsInput

    See

    AWSMachineLearningDescribeTagsOutput

    Declaration

    Objective-C

    - (void)describeTags:(nonnull AWSMachineLearningDescribeTagsInput *)request
        completionHandler:
            (void (^_Nullable)(AWSMachineLearningDescribeTagsOutput *_Nullable,
                               NSError *_Nullable))completionHandler;

    Swift

    func describeTags(_ request: AWSMachineLearningDescribeTagsInput, completionHandler: ((AWSMachineLearningDescribeTagsOutput?, Error?) -> Void)? = nil)

    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.

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

    See

    AWSMachineLearningGetBatchPredictionInput

    See

    AWSMachineLearningGetBatchPredictionOutput

    Declaration

    Objective-C

    - (id)getBatchPrediction:
        (nonnull AWSMachineLearningGetBatchPredictionInput *)request;

    Swift

    func getBatchPrediction(_ request: AWSMachineLearningGetBatchPredictionInput) -> Any!

    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.

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

    See

    AWSMachineLearningGetBatchPredictionInput

    See

    AWSMachineLearningGetBatchPredictionOutput

    Declaration

    Objective-C

    - (void)getBatchPrediction:
                (nonnull AWSMachineLearningGetBatchPredictionInput *)request
             completionHandler:
                 (void (^_Nullable)(
                     AWSMachineLearningGetBatchPredictionOutput *_Nullable,
                     NSError *_Nullable))completionHandler;

    Swift

    func getBatchPrediction(_ request: AWSMachineLearningGetBatchPredictionInput, completionHandler: ((AWSMachineLearningGetBatchPredictionOutput?, Error?) -> Void)? = nil)

    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.

  • Returns a DataSource that includes metadata and data file information, as well as the current status of the DataSource.

    GetDataSource provides results in normal or verbose format. The verbose format adds the schema description and the list of files pointed to by the DataSource to the normal format.

    See

    AWSMachineLearningGetDataSourceInput

    See

    AWSMachineLearningGetDataSourceOutput

    Declaration

    Objective-C

    - (id)getDataSource:(nonnull AWSMachineLearningGetDataSourceInput *)request;

    Swift

    func getDataSource(_ request: AWSMachineLearningGetDataSourceInput) -> Any!

    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.

  • Returns a DataSource that includes metadata and data file information, as well as the current status of the DataSource.

    GetDataSource provides results in normal or verbose format. The verbose format adds the schema description and the list of files pointed to by the DataSource to the normal format.

    See

    AWSMachineLearningGetDataSourceInput

    See

    AWSMachineLearningGetDataSourceOutput

    Declaration

    Objective-C

    - (void)getDataSource:(nonnull AWSMachineLearningGetDataSourceInput *)request
        completionHandler:
            (void (^_Nullable)(AWSMachineLearningGetDataSourceOutput *_Nullable,
                               NSError *_Nullable))completionHandler;

    Swift

    func getDataSource(_ request: AWSMachineLearningGetDataSourceInput, completionHandler: ((AWSMachineLearningGetDataSourceOutput?, Error?) -> Void)? = nil)

    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.

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

    See

    AWSMachineLearningGetEvaluationInput

    See

    AWSMachineLearningGetEvaluationOutput

    Declaration

    Objective-C

    - (id)getEvaluation:(nonnull AWSMachineLearningGetEvaluationInput *)request;

    Swift

    func getEvaluation(_ request: AWSMachineLearningGetEvaluationInput) -> Any!

    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.

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

    See

    AWSMachineLearningGetEvaluationInput

    See

    AWSMachineLearningGetEvaluationOutput

    Declaration

    Objective-C

    - (void)getEvaluation:(nonnull AWSMachineLearningGetEvaluationInput *)request
        completionHandler:
            (void (^_Nullable)(AWSMachineLearningGetEvaluationOutput *_Nullable,
                               NSError *_Nullable))completionHandler;

    Swift

    func getEvaluation(_ request: AWSMachineLearningGetEvaluationInput, completionHandler: ((AWSMachineLearningGetEvaluationOutput?, Error?) -> Void)? = nil)

    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.

  • Returns an MLModel that includes detailed metadata, data source information, and the current status of the MLModel.

    GetMLModel provides results in normal or verbose format.

    See

    AWSMachineLearningGetMLModelInput

    See

    AWSMachineLearningGetMLModelOutput

    Declaration

    Objective-C

    - (id)getMLModel:(nonnull AWSMachineLearningGetMLModelInput *)request;

    Swift

    func getMLModel(_ request: AWSMachineLearningGetMLModelInput) -> Any!

    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.

  • Returns an MLModel that includes detailed metadata, data source information, and the current status of the MLModel.

    GetMLModel provides results in normal or verbose format.

    See

    AWSMachineLearningGetMLModelInput

    See

    AWSMachineLearningGetMLModelOutput

    Declaration

    Objective-C

    - (void)getMLModel:(nonnull AWSMachineLearningGetMLModelInput *)request
        completionHandler:
            (void (^_Nullable)(AWSMachineLearningGetMLModelOutput *_Nullable,
                               NSError *_Nullable))completionHandler;

    Swift

    func getMLModel(_ request: AWSMachineLearningGetMLModelInput, completionHandler: ((AWSMachineLearningGetMLModelOutput?, Error?) -> Void)? = nil)

    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.

  • Generates a prediction for the observation using the specified ML Model.

    Note

    Not all response parameters will be populated. Whether a response parameter is populated depends on the type of model requested.

    See

    AWSMachineLearningPredictInput

    See

    AWSMachineLearningPredictOutput

    Declaration

    Objective-C

    - (id)predict:(nonnull AWSMachineLearningPredictInput *)request;

    Swift

    func predict(_ request: AWSMachineLearningPredictInput) -> Any!

    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.

  • Generates a prediction for the observation using the specified ML Model.

    Note

    Not all response parameters will be populated. Whether a response parameter is populated depends on the type of model requested.

    See

    AWSMachineLearningPredictInput

    See

    AWSMachineLearningPredictOutput

    Declaration

    Objective-C

    - (void)predict:(nonnull AWSMachineLearningPredictInput *)request
        completionHandler:
            (void (^_Nullable)(AWSMachineLearningPredictOutput *_Nullable,
                               NSError *_Nullable))completionHandler;

    Swift

    func predict(_ request: AWSMachineLearningPredictInput, completionHandler: ((AWSMachineLearningPredictOutput?, Error?) -> Void)? = nil)

    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.

  • Updates the BatchPredictionName of a BatchPrediction.

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

    See

    AWSMachineLearningUpdateBatchPredictionInput

    See

    AWSMachineLearningUpdateBatchPredictionOutput

    Declaration

    Objective-C

    - (id)updateBatchPrediction:
        (nonnull AWSMachineLearningUpdateBatchPredictionInput *)request;

    Swift

    func updateBatchPrediction(_ request: AWSMachineLearningUpdateBatchPredictionInput) -> Any!

    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.

  • Updates the BatchPredictionName of a BatchPrediction.

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

    See

    AWSMachineLearningUpdateBatchPredictionInput

    See

    AWSMachineLearningUpdateBatchPredictionOutput

    Declaration

    Objective-C

    - (void)updateBatchPrediction:
                (nonnull AWSMachineLearningUpdateBatchPredictionInput *)request
                completionHandler:
                    (void (^_Nullable)(
                        AWSMachineLearningUpdateBatchPredictionOutput *_Nullable,
                        NSError *_Nullable))completionHandler;

    Swift

    func updateBatchPrediction(_ request: AWSMachineLearningUpdateBatchPredictionInput, completionHandler: ((AWSMachineLearningUpdateBatchPredictionOutput?, Error?) -> Void)? = nil)

    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.

  • Updates the DataSourceName of a DataSource.

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

    See

    AWSMachineLearningUpdateDataSourceInput

    See

    AWSMachineLearningUpdateDataSourceOutput

    Declaration

    Objective-C

    - (id)updateDataSource:
        (nonnull AWSMachineLearningUpdateDataSourceInput *)request;

    Swift

    func updateDataSource(_ request: AWSMachineLearningUpdateDataSourceInput) -> Any!

    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.

  • Updates the DataSourceName of a DataSource.

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

    See

    AWSMachineLearningUpdateDataSourceInput

    See

    AWSMachineLearningUpdateDataSourceOutput

    Declaration

    Objective-C

    - (void)
         updateDataSource:(nonnull AWSMachineLearningUpdateDataSourceInput *)request
        completionHandler:
            (void (^_Nullable)(AWSMachineLearningUpdateDataSourceOutput *_Nullable,
                               NSError *_Nullable))completionHandler;

    Swift

    func updateDataSource(_ request: AWSMachineLearningUpdateDataSourceInput, completionHandler: ((AWSMachineLearningUpdateDataSourceOutput?, Error?) -> Void)? = nil)

    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.

  • Updates the EvaluationName of an Evaluation.

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

    See

    AWSMachineLearningUpdateEvaluationInput

    See

    AWSMachineLearningUpdateEvaluationOutput

    Declaration

    Objective-C

    - (id)updateEvaluation:
        (nonnull AWSMachineLearningUpdateEvaluationInput *)request;

    Swift

    func updateEvaluation(_ request: AWSMachineLearningUpdateEvaluationInput) -> Any!

    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.

  • Updates the EvaluationName of an Evaluation.

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

    See

    AWSMachineLearningUpdateEvaluationInput

    See

    AWSMachineLearningUpdateEvaluationOutput

    Declaration

    Objective-C

    - (void)
         updateEvaluation:(nonnull AWSMachineLearningUpdateEvaluationInput *)request
        completionHandler:
            (void (^_Nullable)(AWSMachineLearningUpdateEvaluationOutput *_Nullable,
                               NSError *_Nullable))completionHandler;

    Swift

    func updateEvaluation(_ request: AWSMachineLearningUpdateEvaluationInput, completionHandler: ((AWSMachineLearningUpdateEvaluationOutput?, Error?) -> Void)? = nil)

    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.

  • Updates the MLModelName and the ScoreThreshold of an MLModel.

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

    See

    AWSMachineLearningUpdateMLModelInput

    See

    AWSMachineLearningUpdateMLModelOutput

    Declaration

    Objective-C

    - (id)updateMLModel:(nonnull AWSMachineLearningUpdateMLModelInput *)request;

    Swift

    func updateMLModel(_ request: AWSMachineLearningUpdateMLModelInput) -> Any!

    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.

  • Updates the MLModelName and the ScoreThreshold of an MLModel.

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

    See

    AWSMachineLearningUpdateMLModelInput

    See

    AWSMachineLearningUpdateMLModelOutput

    Declaration

    Objective-C

    - (void)updateMLModel:(nonnull AWSMachineLearningUpdateMLModelInput *)request
        completionHandler:
            (void (^_Nullable)(AWSMachineLearningUpdateMLModelOutput *_Nullable,
                               NSError *_Nullable))completionHandler;

    Swift

    func updateMLModel(_ request: AWSMachineLearningUpdateMLModelInput, completionHandler: ((AWSMachineLearningUpdateMLModelOutput?, Error?) -> Void)? = nil)

    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.