Scaling and Distributing Python & ML Applications with Ray

Modern machine learning workloads are compute-intensive and require distributed execution. Ray is an open-source, general-purpose, distributed framework that easily scales Python applications and ML workloads from a laptop to a cluster. This talk will cover Ray’s overview, architecture, core concepts, and design patterns. We will demonstrate how Ray can scale training, hyperparameter tuning, and inference from a single node to a cluster, with tangible performance benefits.

Speaker Bio:
Jules S. Damji is a lead developer advocate at Anyscale Inc, an MLflow contributor, and co-author of Learning Spark, 2nd Edition.
He is a hands-on developer with over 25 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).