Learning Ray Flexible Distributed Python for Machine Learning (Sixth Early Release)

Authors Max Pumperla, Edward Oakes, and Richard Liaw show you how to build reinforcement learning applications that serve trained models with Ray. You'll understand how Ray fits into the current landscape of data science tools and discover how this programming language continues to integrate ever more tightly with these tools. Distributed computation is hard, but with Ray you'll find it easy to get started.
Learn how to build your first distributed application with Ray Core
Conduct hyperparameter optimization with Ray Tune
Use the Ray RLib library for reinforcement learning
Manage distributed training with the RaySGD library
Use Ray to perform data processing
Learn how work with Ray Clusters and serve models with Ray Serve
Build an end-to-end machine learning application with Ray