docs-archive/apache-airflow-providers-amazon/3.2.0/_sources/operators/sagemaker.rst.txt

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Amazon SageMaker Operators
========================================

Prerequisite Tasks
------------------

.. include:: _partials/prerequisite_tasks.rst

Overview
--------

Airflow to Amazon SageMaker integration provides several operators to create and interact with
SageMaker Jobs.

  - :class:`~airflow.providers.amazon.aws.operators.sagemaker.SageMakerDeleteModelOperator`
  - :class:`~airflow.providers.amazon.aws.operators.sagemaker.SageMakerModelOperator`
  - :class:`~airflow.providers.amazon.aws.operators.sagemaker.SageMakerProcessingOperator`
  - :class:`~airflow.providers.amazon.aws.operators.sagemaker.SageMakerTrainingOperator`
  - :class:`~airflow.providers.amazon.aws.operators.sagemaker.SageMakerTransformOperator`
  - :class:`~airflow.providers.amazon.aws.operators.sagemaker.SageMakerTuningOperator`

Purpose
"""""""

This example DAG ``example_sagemaker.py`` uses ``SageMakerProcessingOperator``, ``SageMakerTrainingOperator``,
``SageMakerModelOperator``, ``SageMakerDeleteModelOperator`` and ``SageMakerTransformOperator`` to
create SageMaker processing job, run the training job,
generate the models artifact in s3, create the model,
, run SageMaker Batch inference and delete the model from SageMaker.

Defining tasks
""""""""""""""

In the following code we create a SageMaker processing,
training, Sagemaker Model, batch transform job and
then delete the model.

.. exampleinclude:: /../../airflow/providers/amazon/aws/example_dags/example_sagemaker.py
    :language: python
    :start-after: [START howto_operator_sagemaker]
    :end-before: [END howto_operator_sagemaker]

Reference
---------

For further information, look at:

* `Boto3 Library Documentation for Sagemaker <https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker.html>`__