Ray Learning Path

Introduction to Ray

The Ray 101 learning path is the perfect entry point to get you started with Ray. You'll learn about all of our libraries, from low-level distributed computing with Ray Core to running AI workloads with Ray Data, Train, Tune and Serve

Here's what you'll learn:

Get started with Ray and its AI libraries

Understand the basics of distributed computation with Ray Core

Learn to process data at scale with Ray Data

Run distributed machine learning models with Ray Train

Tune your ML hyperparameters with Ray Tune

Learn to serve your models with Ray Serve

Enroll in training

Enroll in this learning path and get started with Ray

10 Lessons

Introduction to Ray and its AI Libraries (Overview)

This course is designed for users new to Ray. It serves as an introductory step in learning Ray, covering fundamentals and providing an overview of its key components.

16 Lessons

Distributed Computation Foundations (Ray Core)

Master the basics of distributed computing with this foundations course on Ray Core.

24 Lessons

Data Processing Foundations (Ray Data)

Explore the foundations of processing structured and unstructured data with Ray Data and get an overview of the industry landscape.

29 Lessons

Model Training Foundations (Ray Train)

Learn the foundations of distributed training of machine learning models with Ray Train.

9 Lessons

Hyperparameter Tuning Foundations (Ray Tune)

This foundation course helps you get started with running your own hyperparameter tuning experiments with Ray Tune.

7 Lessons

Model Serving Foundations (Ray Serve)

Learn to deploy your machine learning models in this foundations course on Ray Serve.