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

Interface MLModel.Builder

    • Method Detail

      • mlModelId

        MLModel.Builder mlModelId​(String mlModelId)

        The ID assigned to the MLModel at creation.

        Parameters:
        mlModelId - The ID assigned to the MLModel at creation.
        Returns:
        Returns a reference to this object so that method calls can be chained together.

      • trainingDataSourceId

        MLModel.Builder trainingDataSourceId​(String trainingDataSourceId)

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

        Parameters:
        trainingDataSourceId - The ID of the training DataSource. The CreateMLModel operation uses the TrainingDataSourceId.
        Returns:
        Returns a reference to this object so that method calls can be chained together.

      • createdByIamUser

        MLModel.Builder createdByIamUser​(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.

        Parameters:
        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:
        Returns a reference to this object so that method calls can be chained together.

      • createdAt

        MLModel.Builder createdAt​(Instant createdAt)

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

        Parameters:
        createdAt - The time that the MLModel was created. The time is expressed in epoch time.
        Returns:
        Returns a reference to this object so that method calls can be chained together.

      • lastUpdatedAt

        MLModel.Builder lastUpdatedAt​(Instant lastUpdatedAt)

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

        Parameters:
        lastUpdatedAt - The time of the most recent edit to the MLModel. The time is expressed in epoch time.
        Returns:
        Returns a reference to this object so that method calls can be chained together.

      • name

        MLModel.Builder name​(String name)

        A user-supplied name or description of the MLModel.

        Parameters:
        name - A user-supplied name or description of the MLModel.
        Returns:
        Returns a reference to this object so that method calls can be chained together.

      • status

        MLModel.Builder status​(String 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.

        Parameters:
        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.

        Returns:
        Returns a reference to this object so that method calls can be chained together.
        See Also:
        EntityStatus, EntityStatus

      • status

        MLModel.Builder status​(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.

        Parameters:
        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.

        Returns:
        Returns a reference to this object so that method calls can be chained together.
        See Also:
        EntityStatus, EntityStatus

      • sizeInBytes

        MLModel.Builder sizeInBytes​(Long sizeInBytes)
        Sets the value of the SizeInBytes property for this object.
        Parameters:
        sizeInBytes - The new value for the SizeInBytes property for this object.
        Returns:
        Returns a reference to this object so that method calls can be chained together.

      • endpointInfo

        MLModel.Builder endpointInfo​(RealtimeEndpointInfo endpointInfo)

        The current endpoint of the MLModel.

        Parameters:
        endpointInfo - The current endpoint of the MLModel.
        Returns:
        Returns a reference to this object so that method calls can be chained together.

      • trainingParameters

        MLModel.Builder trainingParameters​(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.

        Parameters:
        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.

        Returns:
        Returns a reference to this object so that method calls can be chained together.

      • inputDataLocationS3

        MLModel.Builder inputDataLocationS3​(String inputDataLocationS3)

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

        Parameters:
        inputDataLocationS3 - The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
        Returns:
        Returns a reference to this object so that method calls can be chained together.

      • algorithm

        MLModel.Builder algorithm​(String 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.

        Parameters:
        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.

        Returns:
        Returns a reference to this object so that method calls can be chained together.
        See Also:
        Algorithm, Algorithm

      • algorithm

        MLModel.Builder algorithm​(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.

        Parameters:
        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.

        Returns:
        Returns a reference to this object so that method calls can be chained together.
        See Also:
        Algorithm, Algorithm

      • mlModelType

        MLModel.Builder mlModelType​(String 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?".

        Parameters:
        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?".

        Returns:
        Returns a reference to this object so that method calls can be chained together.
        See Also:
        MLModelType, MLModelType

      • mlModelType

        MLModel.Builder mlModelType​(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?".

        Parameters:
        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?".

        Returns:
        Returns a reference to this object so that method calls can be chained together.
        See Also:
        MLModelType, MLModelType

      • scoreThreshold

        MLModel.Builder scoreThreshold​(Float scoreThreshold)
        Sets the value of the ScoreThreshold property for this object.
        Parameters:
        scoreThreshold - The new value for the ScoreThreshold property for this object.
        Returns:
        Returns a reference to this object so that method calls can be chained together.

      • scoreThresholdLastUpdatedAt

        MLModel.Builder scoreThresholdLastUpdatedAt​(Instant scoreThresholdLastUpdatedAt)

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

        Parameters:
        scoreThresholdLastUpdatedAt - The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time.
        Returns:
        Returns a reference to this object so that method calls can be chained together.

      • message

        MLModel.Builder message​(String message)

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

        Parameters:
        message - A description of the most recent details about accessing the MLModel.
        Returns:
        Returns a reference to this object so that method calls can be chained together.

      • computeTime

        MLModel.Builder computeTime​(Long computeTime)
        Sets the value of the ComputeTime property for this object.
        Parameters:
        computeTime - The new value for the ComputeTime property for this object.
        Returns:
        Returns a reference to this object so that method calls can be chained together.

      • finishedAt

        MLModel.Builder finishedAt​(Instant finishedAt)
        Sets the value of the FinishedAt property for this object.
        Parameters:
        finishedAt - The new value for the FinishedAt property for this object.
        Returns:
        Returns a reference to this object so that method calls can be chained together.

      • startedAt

        MLModel.Builder startedAt​(Instant startedAt)
        Sets the value of the StartedAt property for this object.
        Parameters:
        startedAt - The new value for the StartedAt property for this object.
        Returns:
        Returns a reference to this object so that method calls can be chained together.