Anyscale Academy provides training on the Ray ecosystem, tailored for data scientists, data engineers, and MLOps engineers.

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This tutorial introduces you to two of the major Ray libraries: Ray Tune for hyperparameter tuning/optimization (HPO) and Ray Serve for model serving. The Tune tutorial also covers Ray SGD.

Ray Tune and Ray Serve

RLlib is the Ray-based library for implementing reinforcement learning applications. This tutorial will introduce you to RL principles and common algorithms, using a series of example problems.

Reinforcement Learning with Ray RLlib

Ray was created to make it easier to scale diverse computation tasks and distributed state across a cluster, with a minimum of distributed systems expertise and knowledge required. You’ll learn about the particular challenges Ray meets, how Ray works under the hood, how to use Ray in your own applications, and how to debug problems when they occur.

Introduction to Ray: Fast and Simple Distributed Computing

From the creators of Ray, Anyscale is a framework for building machine learning applications at any scale originating from the UC Berkeley RISELab.