AWSMachineLearningMLModel

Objective-C

@interface AWSMachineLearningMLModel

Swift

class AWSMachineLearningMLModel

Represents the output of a GetMLModel operation.

The content consists of the detailed metadata and the current status of the MLModel.

  • The algorithm used to train the MLModel. The following algorithm is supported:

    • SGD – Stochastic gradient descent. The goal of SGD is to minimize the gradient of the loss function.

    Declaration

    Objective-C

    @property (nonatomic) AWSMachineLearningAlgorithm algorithm;

    Swift

    var algorithm: AWSMachineLearningAlgorithm { get set }

  • Long integer type that is a 64-bit signed number.

    Declaration

    Objective-C

    @property (nonatomic, strong) NSNumber *_Nullable computeTime;

    Swift

    var computeTime: NSNumber? { get set }

  • The time that the MLModel was created. The time is expressed in epoch time.

    Declaration

    Objective-C

    @property (nonatomic, strong) NSDate *_Nullable createdAt;

    Swift

    var createdAt: Date? { get set }

  • The AWS user account from which the MLModel was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.

    Declaration

    Objective-C

    @property (nonatomic, strong) NSString *_Nullable createdByIamUser;

    Swift

    var createdByIamUser: String? { get set }

  • The current endpoint of the MLModel.

    Declaration

    Objective-C

    @property (nonatomic, strong) AWSMachineLearningRealtimeEndpointInfo *_Nullable endpointInfo;

    Swift

    var endpointInfo: AWSMachineLearningRealtimeEndpointInfo? { get set }

  • A timestamp represented in epoch time.

    Declaration

    Objective-C

    @property (nonatomic, strong) NSDate *_Nullable finishedAt;

    Swift

    var finishedAt: Date? { get set }

  • The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

    Declaration

    Objective-C

    @property (nonatomic, strong) NSString *_Nullable inputDataLocationS3;

    Swift

    var inputDataLocationS3: String? { get set }

  • The time of the most recent edit to the MLModel. The time is expressed in epoch time.

    Declaration

    Objective-C

    @property (nonatomic, strong) NSDate *_Nullable lastUpdatedAt;

    Swift

    var lastUpdatedAt: Date? { get set }

  • The ID assigned to the MLModel at creation.

    Declaration

    Objective-C

    @property (nonatomic, strong) NSString *_Nullable MLModelId;

    Swift

    var mlModelId: String? { get set }

  • Identifies the MLModel category. The following are the available types:

    • REGRESSION - Produces a numeric result. For example, “What price should a house be listed at?”
    • BINARY - Produces one of two possible results. For example, “Is this a child-friendly web site?”.
    • MULTICLASS - Produces one of several possible results. For example, “Is this a HIGH-, LOW-, or MEDIUM<?oxy_delete author="annbech” timestamp=“20160328T175050-0700” content=“ ”><?oxy_insert_start author=“annbech” timestamp=“20160328T175050-0700”>-<?oxy_insert_end>risk trade?“.

    Declaration

    Objective-C

    @property (nonatomic) AWSMachineLearningMLModelType MLModelType;

    Swift

    var mlModelType: AWSMachineLearningMLModelType { get set }

  • A description of the most recent details about accessing the MLModel.

    Declaration

    Objective-C

    @property (nonatomic, strong) NSString *_Nullable message;

    Swift

    var message: String? { get set }

  • A user-supplied name or description of the MLModel.

    Declaration

    Objective-C

    @property (nonatomic, strong) NSString *_Nullable name;

    Swift

    var name: String? { get set }

  • Declaration

    Objective-C

    @property (nonatomic, strong) NSNumber *_Nullable scoreThreshold;

    Swift

    var scoreThreshold: NSNumber? { get set }

  • The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time.

    Declaration

    Objective-C

    @property (nonatomic, strong) NSDate *_Nullable scoreThresholdLastUpdatedAt;

    Swift

    var scoreThresholdLastUpdatedAt: Date? { get set }

  • Long integer type that is a 64-bit signed number.

    Declaration

    Objective-C

    @property (nonatomic, strong) NSNumber *_Nullable sizeInBytes;

    Swift

    var sizeInBytes: NSNumber? { get set }

  • A timestamp represented in epoch time.

    Declaration

    Objective-C

    @property (nonatomic, strong) NSDate *_Nullable startedAt;

    Swift

    var startedAt: Date? { get set }

  • The current status of an MLModel. This element can have one of the following values:

    • PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create an MLModel.
    • INPROGRESS - The creation process is underway.
    • FAILED - The request to create an MLModel didn’t run to completion. The model isn’t usable.
    • COMPLETED - The creation process completed successfully.
    • DELETED - The MLModel is marked as deleted. It isn’t usable.

    Declaration

    Objective-C

    @property (nonatomic) AWSMachineLearningEntityStatus status;

    Swift

    var status: AWSMachineLearningEntityStatus { get set }

  • The ID of the training DataSource. The CreateMLModel operation uses the TrainingDataSourceId.

    Declaration

    Objective-C

    @property (nonatomic, strong) NSString *_Nullable trainingDataSourceId;

    Swift

    var trainingDataSourceId: String? { get set }

  • A list of the training parameters in the MLModel. The list is implemented as a map of key-value pairs.

    The following is the current set of training parameters:

    • sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.

      The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.

    • sgd.maxPasses - The number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 10000. The default value is 10.

    • sgd.shuffleType - Whether Amazon ML shuffles the training data. Shuffling the data improves a model’s ability to find the optimal solution for a variety of data types. The valid values are auto and none. The default value is none.

    • sgd.l1RegularizationAmount - The coefficient regularization L1 norm, which controls overfitting the data by penalizing large coefficients. This parameter tends to drive coefficients to zero, resulting in sparse feature set. If you use this parameter, start by specifying a small value, such as 1.0E-08.

      The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L1 normalization. This parameter can’t be used when L2 is specified. Use this parameter sparingly.

    • sgd.l2RegularizationAmount - The coefficient regularization L2 norm, which controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as 1.0E-08.

      The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L2 normalization. This parameter can’t be used when L1 is specified. Use this parameter sparingly.

    Declaration

    Objective-C

    @property (nonatomic, strong) NSDictionary<NSString *, NSString *> *_Nullable trainingParameters;

    Swift

    var trainingParameters: [String : String]? { get set }