How to set model custom metadata in Sagemaker ML pipeline

That is correct. Figured that. Looks like one to call that API is to have LambaStep and chain it after RegisterModel step . Here is lambda implementation. Feels a bit heavy workaround for this objective. So let me know if there is a better /lighter way to achieving the same. Thanks.

sagemaker_client = client = boto3.client("sagemaker")
def lambda_handler(event, context):

    print(boto3.__version__)
    logger.info(f"Received Event: {event}")
    logger.info(f"Boto version: {boto3.__version__}")
    
    model_arn = event['model_arn']
    model_arn = 'arn:aws:sagemaker:us-east-1:ACCOUNT:model-package/model_package_name/version'
    response = client.describe_model_package(
        ModelPackageName=model_arn
    )
    help(client.update_model_package)
    response = client.update_model_package(
        ModelPackageArn=model_arn,
        ModelApprovalStatus=response['ModelApprovalStatus'],
        CustomerMetadataProperties={'string': 'string'},
    )

    return {"statusCode": 200, "body": json.dumps("Model metadata updated successfully")}

Hi,

You can use the UpdateModelPackage API to update custom metadata properties on an already registered model package whether it's in a pipeline or not.