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

Interface CreateMlModelRequest.Builder

All Superinterfaces:
AwsRequest.Builder, Buildable, CopyableBuilder<CreateMlModelRequest.Builder,CreateMlModelRequest>, MachineLearningRequest.Builder, SdkBuilder<CreateMlModelRequest.Builder,CreateMlModelRequest>, SdkPojo, SdkRequest.Builder
Enclosing class:
CreateMlModelRequest
public static interface CreateMlModelRequest.Builder extends MachineLearningRequest.Builder, SdkPojo, CopyableBuilder<CreateMlModelRequest.Builder,CreateMlModelRequest>

  • Method Details

    • mlModelId

      CreateMlModelRequest.Builder mlModelId(String mlModelId)

      A user-supplied ID that uniquely identifies the MLModel.

      Parameters:
      mlModelId - A user-supplied ID that uniquely identifies the MLModel.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • mlModelName

      CreateMlModelRequest.Builder mlModelName(String mlModelName)

      A user-supplied name or description of the MLModel.

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

    • mlModelType

      CreateMlModelRequest.Builder mlModelType(String mlModelType)

      The category of supervised learning that this MLModel will address. Choose from the following types:

      • Choose REGRESSION if the MLModel will be used to predict a numeric value.

      • Choose BINARY if the MLModel result has two possible values.

      • Choose MULTICLASS if the MLModel result has a limited number of values.

      For more information, see the Amazon Machine Learning Developer Guide.

      Parameters:
      mlModelType - The category of supervised learning that this MLModel will address. Choose from the following types:

      • Choose REGRESSION if the MLModel will be used to predict a numeric value.

      • Choose BINARY if the MLModel result has two possible values.

      • Choose MULTICLASS if the MLModel result has a limited number of values.

      For more information, see the Amazon Machine Learning Developer Guide.

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

    • mlModelType

      CreateMlModelRequest.Builder mlModelType(MLModelType mlModelType)

      The category of supervised learning that this MLModel will address. Choose from the following types:

      • Choose REGRESSION if the MLModel will be used to predict a numeric value.

      • Choose BINARY if the MLModel result has two possible values.

      • Choose MULTICLASS if the MLModel result has a limited number of values.

      For more information, see the Amazon Machine Learning Developer Guide.

      Parameters:
      mlModelType - The category of supervised learning that this MLModel will address. Choose from the following types:

      • Choose REGRESSION if the MLModel will be used to predict a numeric value.

      • Choose BINARY if the MLModel result has two possible values.

      • Choose MULTICLASS if the MLModel result has a limited number of values.

      For more information, see the Amazon Machine Learning Developer Guide.

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

    • parameters

      CreateMlModelRequest.Builder parameters(Map<String,String> parameters)

      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. We strongly recommend that you shuffle your data.

      • sgd.l1RegularizationAmount - The coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a 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. It 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:
      parameters - 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. We strongly recommend that you shuffle your data.

      • sgd.l1RegularizationAmount - The coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a 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. It 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.

    • trainingDataSourceId

      CreateMlModelRequest.Builder trainingDataSourceId(String trainingDataSourceId)

      The DataSource that points to the training data.

      Parameters:
      trainingDataSourceId - The DataSource that points to the training data.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • recipe

      CreateMlModelRequest.Builder recipe(String recipe)

      The data recipe for creating the MLModel. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default.

      Parameters:
      recipe - The data recipe for creating the MLModel. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • recipeUri

      CreateMlModelRequest.Builder recipeUri(String recipeUri)

      The Amazon Simple Storage Service (Amazon S3) location and file name that contains the MLModel recipe. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default.

      Parameters:
      recipeUri - The Amazon Simple Storage Service (Amazon S3) location and file name that contains the MLModel recipe. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • overrideConfiguration

      CreateMlModelRequest.Builder overrideConfiguration(AwsRequestOverrideConfiguration overrideConfiguration)
      Description copied from interface: AwsRequest.Builder
      Add an optional request override configuration.
      Specified by:
      overrideConfiguration in interface AwsRequest.Builder
      Parameters:
      overrideConfiguration - The override configuration.
      Returns:
      This object for method chaining.

    • overrideConfiguration

      CreateMlModelRequest.Builder overrideConfiguration(Consumer<AwsRequestOverrideConfiguration.Builder> builderConsumer)
      Description copied from interface: AwsRequest.Builder
      Add an optional request override configuration.
      Specified by:
      overrideConfiguration in interface AwsRequest.Builder
      Parameters:
      builderConsumer - A Consumer to which an empty AwsRequestOverrideConfiguration.Builder will be given.
      Returns:
      This object for method chaining.