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

Learn the end-to-end multi-modal AI pipeline, including how each component fits together and what the project provides. You’ll also gain hands-on experience running and exploring the implementation using Anyscale or the GitHub repository.

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

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

Explore the basics of observability with Ray and Anyscale in this foundations course.

Explore the foundations of processing structured and unstructured data with Ray Data and get an overview of the key concepts and patterns for distributed data processing.

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.

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

A recommender systems workload example built with Ray Train.

This course helps developers kick-start their work on Anyscale.

Learn the fundamentals of deploying LLMs with Ray.

An example of online model serving implemented with Ray Serve.

A policy learning workload example utilizing Ray Train.

A generative computer vision workload example built with Ray Train.

An example of distributed model training using Ray Train.

A time series data workload example built with Ray Train.

A tabular data workload example built with Ray Train.

An example of distributed model training using Ray Train.

An example of batch inference implemented with Ray Data.

An example of data processing using Ray Data.

A workload example demonstrating model training with PyTorch and Lightning.

This course helps admins deploy Anyscale clouds in custom environments (AWS vs. GCP, VMs vs. Kubernetes, etc.).

Capstone certificate exam for Introduction to Ray.