In this workshop, you have seen that building an ML workflow involves quite a bit of preparation but it helps improve the rate of experimentation, engineering productivity, and maintenance of repetitive ML tasks. Airflow Amazon SageMaker Operators provide a convenient way to build ML workflows and integrate with Amazon SageMaker.

You can extend the workflows by customizing the Airflow DAGs with any tasks that better fit your ML workflows, such as feature engineering, creating an ensemble of training models, creating parallel training jobs, and retraining models to adapt to the data distribution changes.