This course is designed for users new to Ray. It serves as an introductory step in learning Ray, covering fundamentals of the Ray ecosystem and its AI libraries.
In this intro module, you'll get an overview of what Ray is and how the course will help you scale Python applications with distributed computing. You'll also set up a reproducible local Ray + Jupyter development environment and verify your installation by running a simple Ray task.
Learn how the Ray AI Libraries (Ray Data, Train, Tune, and Serve) fit together to build scalable ML workflows. You’ll walk through an end-to-end XGBoost regression example on NYC taxi data, covering data loading, distributed hyperparameter tuning, and training.
