This is an introductory and interactive session on concepts and code on Ray Core and Ray’s Ecosystem. The session covers why use and adopt Ray, touches upon Ray’s Ecosystem for scaling machine learning (ML) workloads, and provides an overview of Ray’s architecture and components.
We’ll also cover Ray Core APIs to write remote functions, actors and understand Ray’s basic design patterns for writing distributed Python applications.
By the end of this session, you will learn:
Jules S. Damji is a lead developer advocate at Anyscale and an MLflow contributor. He is a hands-on developer with over 20+ years of experience and has worked at leading companies, such as Sun Microsystems, Netscape, @Home, Opsware/Loudcloud, VeriSign, ProQuest, Hortonworks, and Databricks, building large-scale distributed systems. He holds a B.Sc and M.Sc in Computer Science (from Oregon State University and Cal State, Chico respectively), and an MA in Political Advocacy and Communication (from Johns Hopkins University).