modules/pipeline/inference_pipeline_define.py [41:68]:
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        }
    )

    """
    Create Preprocessing Model from model artifact.
    """
    # sagemaker_session = sagemaker.Session()

    sklearn_processor = SKLearnProcessor(
        framework_version="0.20.0",
        role=sm_role,
        instance_type="ml.m5.xlarge",
        instance_count=1,
        max_runtime_in_seconds=1200,
    )
    # Create ProcessingInputs and ProcessingOutputs objects for Inputs and
    # Outputs respectively for the SageMaker Processing Job
    inputs = [
        ProcessingInput(
            source=execution_input["InputDataURL"],
            destination="/opt/ml/processing/input",
            input_name="input-1",
        ),
        ProcessingInput(
            source=execution_input["PreprocessingCodeURL"],
            destination="/opt/ml/processing/input/code",
            input_name="code",
        ),
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modules/pipeline/training_pipeline_define.py [52:78]:
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        }
    )

    """
    Data pre-processing and feature engineering
    """
    sklearn_processor = SKLearnProcessor(
        framework_version="0.20.0",
        role=sm_role,
        instance_type="ml.m5.xlarge",
        instance_count=1,
        max_runtime_in_seconds=1200,
    )

    # Create ProcessingInputs and ProcessingOutputs objects for Inputs and
    # Outputs respectively for the SageMaker Processing Job
    inputs = [
        ProcessingInput(
            source=execution_input["InputDataURL"],
            destination="/opt/ml/processing/input",
            input_name="input-1",
        ),
        ProcessingInput(
            source=execution_input["PreprocessingCodeURL"],
            destination="/opt/ml/processing/input/code",
            input_name="code",
        ),
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