AWSMachineLearning

The service configuration used to instantiate this service client.

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

Returns the singleton service client. If the singleton object does not exist, the SDK instantiates the default service client with defaultServiceConfiguration from [AWSServiceManager defaultServiceManager]. The reference to this object is maintained by the SDK, and you do not need to retain it manually.

For example, set the default service configuration in - application:didFinishLaunchingWithOptions:

Swift

func application(_ application: UIApplication, didFinishLaunchingWithOptions launchOptions: [UIApplicationLaunchOptionsKey: Any]?) -> Bool {
   let credentialProvider = AWSCognitoCredentialsProvider(regionType: .USEast1, identityPoolId: "YourIdentityPoolId")
   let configuration = AWSServiceConfiguration(region: .USEast1, credentialsProvider: credentialProvider)
   AWSServiceManager.default().defaultServiceConfiguration = configuration

   return true

}

Objective-C

- (BOOL)application:(UIApplication *)application didFinishLaunchingWithOptions:(NSDictionary *)launchOptions {
     AWSCognitoCredentialsProvider *credentialsProvider = [[AWSCognitoCredentialsProvider alloc] initWithRegionType:AWSRegionUSEast1
                                                                                                     identityPoolId:@"YourIdentityPoolId"];
     AWSServiceConfiguration *configuration = [[AWSServiceConfiguration alloc] initWithRegion:AWSRegionUSEast1
                                                                          credentialsProvider:credentialsProvider];
     [AWSServiceManager defaultServiceManager].defaultServiceConfiguration = configuration;

     return YES;
 }

Then call the following to get the default service client:

Swift

let MachineLearning = AWSMachineLearning.default()

Objective-C

AWSMachineLearning *MachineLearning = [AWSMachineLearning defaultMachineLearning];

+ (nonnull instancetype)defaultMachineLearning;

class func `default`() -> Self

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"];

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

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

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"];

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

convenience init(forKey key: String)

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

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

class func remove(forKey key: String)

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.

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

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

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

func addTags(_ request: AWSMachineLearningAddTagsInput) async throws -> AWSMachineLearningAddTagsOutput

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.

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

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

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

func createBatchPrediction(_ request: AWSMachineLearningCreateBatchPredictionInput) async throws -> AWSMachineLearningCreateBatchPredictionOutput

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.

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

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

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

func createDataSource(fromRDS request: AWSMachineLearningCreateDataSourceFromRDSInput) async throws -> AWSMachineLearningCreateDataSourceFromRDSOutput

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>

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

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

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

func createDataSource(fromRedshift request: AWSMachineLearningCreateDataSourceFromRedshiftInput) async throws -> AWSMachineLearningCreateDataSourceFromRedshiftOutput

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.

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

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

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

func createDataSource(fromS3 request: AWSMachineLearningCreateDataSourceFromS3Input) async throws -> AWSMachineLearningCreateDataSourceFromS3Output

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.

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

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

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

func createEvaluation(_ request: AWSMachineLearningCreateEvaluationInput) async throws -> AWSMachineLearningCreateEvaluationOutput

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.

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

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

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

func createMLModel(_ request: AWSMachineLearningCreateMLModelInput) async throws -> AWSMachineLearningCreateMLModelOutput

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.

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

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

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

func createRealtimeEndpoint(_ request: AWSMachineLearningCreateRealtimeEndpointInput) async throws -> AWSMachineLearningCreateRealtimeEndpointOutput

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.

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

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

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

func deleteBatchPrediction(_ request: AWSMachineLearningDeleteBatchPredictionInput) async throws -> AWSMachineLearningDeleteBatchPredictionOutput

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.

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

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

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

func deleteDataSource(_ request: AWSMachineLearningDeleteDataSourceInput) async throws -> AWSMachineLearningDeleteDataSourceOutput

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.

The results of the DeleteEvaluation operation are irreversible.

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

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

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.

The results of the DeleteEvaluation operation are irreversible.

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

func deleteEvaluation(_ request: AWSMachineLearningDeleteEvaluationInput) async throws -> AWSMachineLearningDeleteEvaluationOutput

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.

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

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

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

func deleteMLModel(_ request: AWSMachineLearningDeleteMLModelInput) async throws -> AWSMachineLearningDeleteMLModelOutput

Deletes a real time endpoint of an MLModel.

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

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

Deletes a real time endpoint of an MLModel.

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

func deleteRealtimeEndpoint(_ request: AWSMachineLearningDeleteRealtimeEndpointInput) async throws -> AWSMachineLearningDeleteRealtimeEndpointOutput

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.

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

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

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

func deleteTags(_ request: AWSMachineLearningDeleteTagsInput) async throws -> AWSMachineLearningDeleteTagsOutput

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

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

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

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

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

func describeBatchPredictions(_ request: AWSMachineLearningDescribeBatchPredictionsInput) async throws -> AWSMachineLearningDescribeBatchPredictionsOutput

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

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

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

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

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

func describeDataSources(_ request: AWSMachineLearningDescribeDataSourcesInput) async throws -> AWSMachineLearningDescribeDataSourcesOutput

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

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

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

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

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

func describeEvaluations(_ request: AWSMachineLearningDescribeEvaluationsInput) async throws -> AWSMachineLearningDescribeEvaluationsOutput

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

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

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

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

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

func describeMLModels(_ request: AWSMachineLearningDescribeMLModelsInput) async throws -> AWSMachineLearningDescribeMLModelsOutput

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

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

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

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

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

func describeTags(_ request: AWSMachineLearningDescribeTagsInput) async throws -> AWSMachineLearningDescribeTagsOutput

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

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

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

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

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

func batchPrediction(_ request: AWSMachineLearningGetBatchPredictionInput) async throws -> AWSMachineLearningGetBatchPredictionOutput

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.

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

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

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

func dataSource(_ request: AWSMachineLearningGetDataSourceInput) async throws -> AWSMachineLearningGetDataSourceOutput

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

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

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

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

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

func evaluation(_ request: AWSMachineLearningGetEvaluationInput) async throws -> AWSMachineLearningGetEvaluationOutput

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.

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

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

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

func mlModel(_ request: AWSMachineLearningGetMLModelInput) async throws -> AWSMachineLearningGetMLModelOutput

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

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

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

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

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

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

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

func predict(_ request: AWSMachineLearningPredictInput) async throws -> AWSMachineLearningPredictOutput

Updates the BatchPredictionName of a BatchPrediction.

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

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

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

Updates the BatchPredictionName of a BatchPrediction.

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

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

func updateBatchPrediction(_ request: AWSMachineLearningUpdateBatchPredictionInput) async throws -> AWSMachineLearningUpdateBatchPredictionOutput

Updates the DataSourceName of a DataSource.

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

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

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

Updates the DataSourceName of a DataSource.

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

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

func updateDataSource(_ request: AWSMachineLearningUpdateDataSourceInput) async throws -> AWSMachineLearningUpdateDataSourceOutput

Updates the EvaluationName of an Evaluation.

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

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

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

Updates the EvaluationName of an Evaluation.

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

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

func updateEvaluation(_ request: AWSMachineLearningUpdateEvaluationInput) async throws -> AWSMachineLearningUpdateEvaluationOutput

Updates the MLModelName and the ScoreThreshold of an MLModel.

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

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

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

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

func updateMLModel(_ request: AWSMachineLearningUpdateMLModelInput) async throws -> AWSMachineLearningUpdateMLModelOutput