Interface AutoMLJobChannel.Builder

All Superinterfaces:
Buildable, CopyableBuilder<AutoMLJobChannel.Builder,AutoMLJobChannel>, SdkBuilder<AutoMLJobChannel.Builder,AutoMLJobChannel>, SdkPojo
Enclosing class:
AutoMLJobChannel
public static interface AutoMLJobChannel.Builder extends SdkPojo, CopyableBuilder<AutoMLJobChannel.Builder,AutoMLJobChannel>

  • Method Details

    • channelType

      AutoMLJobChannel.Builder channelType(String channelType)

      The type of channel. Defines whether the data are used for training or validation. The default value is training. Channels for training and validation must share the same ContentType

      The type of channel defaults to training for the time-series forecasting problem type.

      Parameters:
      channelType - The type of channel. Defines whether the data are used for training or validation. The default value is training. Channels for training and validation must share the same ContentType

      The type of channel defaults to training for the time-series forecasting problem type.

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

    • channelType

      AutoMLJobChannel.Builder channelType(AutoMLChannelType channelType)

      The type of channel. Defines whether the data are used for training or validation. The default value is training. Channels for training and validation must share the same ContentType

      The type of channel defaults to training for the time-series forecasting problem type.

      Parameters:
      channelType - The type of channel. Defines whether the data are used for training or validation. The default value is training. Channels for training and validation must share the same ContentType

      The type of channel defaults to training for the time-series forecasting problem type.

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

    • contentType

      AutoMLJobChannel.Builder contentType(String contentType)

      The content type of the data from the input source. The following are the allowed content types for different problems:

      • For tabular problem types: text/csv;header=present or x-application/vnd.amazon+parquet. The default value is text/csv;header=present.

      • For image classification: image/png, image/jpeg, or image/*. The default value is image/*.

      • For text classification: text/csv;header=present or x-application/vnd.amazon+parquet. The default value is text/csv;header=present.

      • For time-series forecasting: text/csv;header=present or x-application/vnd.amazon+parquet. The default value is text/csv;header=present.

      • For text generation (LLMs fine-tuning): text/csv;header=present or x-application/vnd.amazon+parquet. The default value is text/csv;header=present.

      Parameters:
      contentType - The content type of the data from the input source. The following are the allowed content types for different problems:

      • For tabular problem types: text/csv;header=present or x-application/vnd.amazon+parquet. The default value is text/csv;header=present.

      • For image classification: image/png, image/jpeg, or image/*. The default value is image/*.

      • For text classification: text/csv;header=present or x-application/vnd.amazon+parquet. The default value is text/csv;header=present.

      • For time-series forecasting: text/csv;header=present or x-application/vnd.amazon+parquet. The default value is text/csv;header=present.

      • For text generation (LLMs fine-tuning): text/csv;header=present or x-application/vnd.amazon+parquet. The default value is text/csv;header=present.

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

    • compressionType

      AutoMLJobChannel.Builder compressionType(String compressionType)

      The allowed compression types depend on the input format and problem type. We allow the compression type Gzip for S3Prefix inputs on tabular data only. For all other inputs, the compression type should be None. If no compression type is provided, we default to None.

      Parameters:
      compressionType - The allowed compression types depend on the input format and problem type. We allow the compression type Gzip for S3Prefix inputs on tabular data only. For all other inputs, the compression type should be None. If no compression type is provided, we default to None.
      Returns:
      Returns a reference to this object so that method calls can be chained together.
      See Also:

    • compressionType

      AutoMLJobChannel.Builder compressionType(CompressionType compressionType)

      The allowed compression types depend on the input format and problem type. We allow the compression type Gzip for S3Prefix inputs on tabular data only. For all other inputs, the compression type should be None. If no compression type is provided, we default to None.

      Parameters:
      compressionType - The allowed compression types depend on the input format and problem type. We allow the compression type Gzip for S3Prefix inputs on tabular data only. For all other inputs, the compression type should be None. If no compression type is provided, we default to None.
      Returns:
      Returns a reference to this object so that method calls can be chained together.
      See Also:

    • dataSource

      AutoMLJobChannel.Builder dataSource(AutoMLDataSource dataSource)

      The data source for an AutoML channel (Required).

      Parameters:
      dataSource - The data source for an AutoML channel (Required).
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • dataSource

      default AutoMLJobChannel.Builder dataSource(Consumer<AutoMLDataSource.Builder> dataSource)

      The data source for an AutoML channel (Required).

      This is a convenience method that creates an instance of the AutoMLDataSource.Builder avoiding the need to create one manually via AutoMLDataSource.builder().

      When the Consumer completes, SdkBuilder.build() is called immediately and its result is passed to dataSource(AutoMLDataSource).

      Parameters:
      dataSource - a consumer that will call methods on AutoMLDataSource.Builder
      Returns:
      Returns a reference to this object so that method calls can be chained together.
      See Also: