Package software.amazon.awssdk.services.machinelearning.model

Class MLModel

java.lang.Object
software.amazon.awssdk.services.machinelearning.model.MLModel
All Implemented Interfaces:
Serializable, SdkPojo, ToCopyableBuilder<MLModel.Builder,MLModel>
@Generated("software.amazon.awssdk:codegen") public final class MLModel extends Object implements SdkPojo, Serializable, ToCopyableBuilder<MLModel.Builder,MLModel>

Represents the output of a GetMLModel operation.

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

See Also:

  • Method Details

    • mlModelId

      public final String mlModelId()

      The ID assigned to the MLModel at creation.

      Returns:
      The ID assigned to the MLModel at creation.

    • trainingDataSourceId

      public final String trainingDataSourceId()

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

      Returns:
      The ID of the training DataSource. The CreateMLModel operation uses the TrainingDataSourceId.

    • createdByIamUser

      public final String createdByIamUser()

      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.

      Returns:
      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.

    • createdAt

      public final Instant createdAt()

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

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

    • lastUpdatedAt

      public final Instant lastUpdatedAt()

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

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

    • name

      public final String name()

      A user-supplied name or description of the MLModel.

      Returns:
      A user-supplied name or description of the MLModel.

    • status

      public final EntityStatus status()

      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.

      If the service returns an enum value that is not available in the current SDK version, status will return EntityStatus.UNKNOWN_TO_SDK_VERSION. The raw value returned by the service is available from statusAsString().

      Returns:
      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.

      See Also:

    • statusAsString

      public final String statusAsString()

      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.

      If the service returns an enum value that is not available in the current SDK version, status will return EntityStatus.UNKNOWN_TO_SDK_VERSION. The raw value returned by the service is available from statusAsString().

      Returns:
      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.

      See Also:

    • sizeInBytes

      public final Long sizeInBytes()
      Returns the value of the SizeInBytes property for this object.
      Returns:
      The value of the SizeInBytes property for this object.

    • endpointInfo

      public final RealtimeEndpointInfo endpointInfo()

      The current endpoint of the MLModel.

      Returns:
      The current endpoint of the MLModel.

    • hasTrainingParameters

      public final boolean hasTrainingParameters()
      For responses, this returns true if the service returned a value for the TrainingParameters property. This DOES NOT check that the value is non-empty (for which, you should check the isEmpty() method on the property). This is useful because the SDK will never return a null collection or map, but you may need to differentiate between the service returning nothing (or null) and the service returning an empty collection or map. For requests, this returns true if a value for the property was specified in the request builder, and false if a value was not specified.

    • trainingParameters

      public final Map<String,String> trainingParameters()

      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.

      Attempts to modify the collection returned by this method will result in an UnsupportedOperationException.

      This method will never return null. If you would like to know whether the service returned this field (so that you can differentiate between null and empty), you can use the hasTrainingParameters() method.

      Returns:
      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.

    • inputDataLocationS3

      public final String inputDataLocationS3()

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

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

    • algorithm

      public final Algorithm algorithm()

      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.

      If the service returns an enum value that is not available in the current SDK version, algorithm will return Algorithm.UNKNOWN_TO_SDK_VERSION. The raw value returned by the service is available from algorithmAsString().

      Returns:
      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.

      See Also:

    • algorithmAsString

      public final String algorithmAsString()

      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.

      If the service returns an enum value that is not available in the current SDK version, algorithm will return Algorithm.UNKNOWN_TO_SDK_VERSION. The raw value returned by the service is available from algorithmAsString().

      Returns:
      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.

      See Also:

    • mlModelType

      public final MLModelType mlModelType()

      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-risk trade?".

      If the service returns an enum value that is not available in the current SDK version, mlModelType will return MLModelType.UNKNOWN_TO_SDK_VERSION. The raw value returned by the service is available from mlModelTypeAsString().

      Returns:
      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-risk trade?".

      See Also:

    • mlModelTypeAsString

      public final String mlModelTypeAsString()

      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-risk trade?".

      If the service returns an enum value that is not available in the current SDK version, mlModelType will return MLModelType.UNKNOWN_TO_SDK_VERSION. The raw value returned by the service is available from mlModelTypeAsString().

      Returns:
      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-risk trade?".

      See Also:

    • scoreThreshold

      public final Float scoreThreshold()
      Returns the value of the ScoreThreshold property for this object.
      Returns:
      The value of the ScoreThreshold property for this object.

    • scoreThresholdLastUpdatedAt

      public final Instant scoreThresholdLastUpdatedAt()

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

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

    • message

      public final String message()

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

      Returns:
      A description of the most recent details about accessing the MLModel.

    • computeTime

      public final Long computeTime()
      Returns the value of the ComputeTime property for this object.
      Returns:
      The value of the ComputeTime property for this object.

    • finishedAt

      public final Instant finishedAt()
      Returns the value of the FinishedAt property for this object.
      Returns:
      The value of the FinishedAt property for this object.

    • startedAt

      public final Instant startedAt()
      Returns the value of the StartedAt property for this object.
      Returns:
      The value of the StartedAt property for this object.

    • toBuilder

      public MLModel.Builder toBuilder()
      Description copied from interface: ToCopyableBuilder
      Take this object and create a builder that contains all of the current property values of this object.
      Specified by:
      toBuilder in interface ToCopyableBuilder<MLModel.Builder,MLModel>
      Returns:
      a builder for type T

    • builder

      public static MLModel.Builder builder()

    • serializableBuilderClass

      public static Class<? extends MLModel.Builder> serializableBuilderClass()

    • hashCode

      public final int hashCode()
      Overrides:
      hashCode in class Object

    • equals

      public final boolean equals(Object obj)
      Overrides:
      equals in class Object

    • equalsBySdkFields

      public final boolean equalsBySdkFields(Object obj)
      Description copied from interface: SdkPojo
      Indicates whether some other object is "equal to" this one by SDK fields. An SDK field is a modeled, non-inherited field in an SdkPojo class, and is generated based on a service model.

      If an SdkPojo class does not have any inherited fields, equalsBySdkFields and equals are essentially the same.

      Specified by:
      equalsBySdkFields in interface SdkPojo
      Parameters:
      obj - the object to be compared with
      Returns:
      true if the other object equals to this object by sdk fields, false otherwise.

    • toString

      public final String toString()
      Returns a string representation of this object. This is useful for testing and debugging. Sensitive data will be redacted from this string using a placeholder value.
      Overrides:
      toString in class Object

    • getValueForField

      public final <T> Optional<T> getValueForField(String fieldName, Class<T> clazz)

    • sdkFields

      public final List<SdkField<?>> sdkFields()
      Specified by:
      sdkFields in interface SdkPojo
      Returns:
      List of SdkField in this POJO. May be empty list but should never be null.