5. MLOps SageMaker Project/sagemaker-workshop-build-seedcode-v1/pipelines/train/pipeline.py [43:84]:
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)
from sagemaker.workflow.step_collections import RegisterModel


BASE_DIR = os.path.dirname(os.path.realpath(__file__))

def get_sagemaker_client(region):
    """Gets the sagemaker client.

        Args:
            region: the aws region to start the session
            default_bucket: the bucket to use for storing the artifacts

        Returns:
            `sagemaker.session.Session instance
    """
    boto_session = boto3.Session(region_name=region)
    sagemaker_client = boto_session.client("sagemaker")
    return sagemaker_client


def get_session(region, default_bucket):
    """Gets the sagemaker session based on the region.

    Args:
        region: the aws region to start the session
        default_bucket: the bucket to use for storing the artifacts

    Returns:
        `sagemaker.session.Session instance
    """

    boto_session = boto3.Session(region_name=region)

    sagemaker_client = boto_session.client("sagemaker")
    runtime_client = boto_session.client("sagemaker-runtime")
    return sagemaker.session.Session(
        boto_session=boto_session,
        sagemaker_client=sagemaker_client,
        sagemaker_runtime_client=runtime_client,
        default_bucket=default_bucket,
    )
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5. MLOps SageMaker Project/sagemaker-workshop-preprocess-seedcode-v1/pipelines/preprocess/pipeline.py [27:67]:
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)


BASE_DIR = os.path.dirname(os.path.realpath(__file__))

def get_sagemaker_client(region):
    """Gets the sagemaker client.

        Args:
            region: the aws region to start the session
            default_bucket: the bucket to use for storing the artifacts

        Returns:
            `sagemaker.session.Session instance
    """
    boto_session = boto3.Session(region_name=region)
    sagemaker_client = boto_session.client("sagemaker")
    return sagemaker_client


def get_session(region, default_bucket):
    """Gets the sagemaker session based on the region.

    Args:
        region: the aws region to start the session
        default_bucket: the bucket to use for storing the artifacts

    Returns:
        `sagemaker.session.Session instance
    """

    boto_session = boto3.Session(region_name=region)

    sagemaker_client = boto_session.client("sagemaker")
    runtime_client = boto_session.client("sagemaker-runtime")
    return sagemaker.session.Session(
        boto_session=boto_session,
        sagemaker_client=sagemaker_client,
        sagemaker_runtime_client=runtime_client,
        default_bucket=default_bucket,
    )
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