5. MLOps SageMaker Project/sagemaker-workshop-build-seedcode-v1/pipelines/train/pipeline.py [99:120]:
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def get_pipeline(
    region,
    sagemaker_project_arn=None,
    role=None,
    default_bucket=None,
    model_package_group_name="DYCTaxiPackageGroup",
    pipeline_name="DYCTrainPipeline",
    base_job_prefix="DYCTaxiTrain",
):
    """Gets a SageMaker ML Pipeline instance working with on abalone data.

    Args:
        region: AWS region to create and run the pipeline.
        role: IAM role to create and run steps and pipeline.
        default_bucket: the bucket to use for storing the artifacts

    Returns:
        an instance of a pipeline
    """
    sagemaker_session = get_session(region, default_bucket)
    if role is None:
        role = sagemaker.session.get_execution_role(sagemaker_session)
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5. MLOps SageMaker Project/sagemaker-workshop-deploy-seedcode-v1/pipelines/deploy/pipeline.py [91:112]:
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def get_pipeline(
    region,
    sagemaker_project_arn=None,
    role=None,
    default_bucket=None,
    model_package_group_name="DYCTaxiPackageGroup",
    pipeline_name="DYCTrainPipeline",
    base_job_prefix="DYCTaxiTrain",
):
    """Gets a SageMaker ML Pipeline instance working with on abalone data.

    Args:
        region: AWS region to create and run the pipeline.
        role: IAM role to create and run steps and pipeline.
        default_bucket: the bucket to use for storing the artifacts

    Returns:
        an instance of a pipeline
    """
    sagemaker_session = get_session(region, default_bucket)
    if role is None:
        role = sagemaker.session.get_execution_role(sagemaker_session)
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