Ray: The Next Generation Compute Runtime for ML Applications

Ray is an open source project that makes it simple to scale any compute-intensive Python workload. Industry leaders like Uber, Shopify, Spotify are building their next generation ML platforms on top of Ray. Ray is equipped with a powerful distributed scheduling mechanism which launches stateful Actors and stateless Tasks in a much more granular and lightweight fashion than existing frameworks. Meanwhile it also has an embedded distributed in-memory object store to drastically reduce data exchange overhead. These architectural advantages make Ray the ideal compute substrate for cutting-edge ML use cases including Graph Neural Networks, Online Learning, Reinforcement Learning, and so forth.

This talk will introduce the basic API and architectural concepts of Ray, as well as diving deeper into some of its innovative ML use cases.


Speaker

Zhe Zhang

Head of Open Source Engineering @anyscalecompute

Zhe is currently Head of Open Source Engineering (Ray.io project) at Anyscale. Before Anyscale, Zhe spent 4.5 years at LinkedIn where he managed the Hadoop/Spark infra team. Zhe has been working on open source for about 10 years; he's a committer and PMC member of the Apache Hadoop project, and a member of the Apache Software Foundation.

Read more
Find Zhe Zhang at:

From the same track

Session

Fabricator: End-to-End Declarative Feature Engineering Platform

At Doordash, the last year has seen a surge in applications of machine learning to various product verticals in our growing business. However, with this growth, our data scientists have had increasing bottlenecks in their development cycle because of our existing feature engineering process.

Kunal Shah

ML Platform Engineering Manager @DoorDash

Session

An Open Source Infrastructure for PyTorch

In this talk we’ll go over tools and techniques to deploy PyTorch in production. The PyTorch organization maintains and supports open source tools for efficient inference like pytorch/serve, job management pytorch/torchx and streaming datasets like pytorch/data.

Mark Saroufim

Applied AI Engineer @Meta

Session

Metrics for MLOps Platforms

Many companies are investing heavily into their ML platforms, either building something in-house or working with vendors. How do we know that an ML platform is any good? How do we compare different platforms?

Chip Huyen

Co-founder @Claypot AI

Session

Empower Your ML Models with Customers Voice

ML engineers use A/B testings to iterate ML models, however, there are limitations of A/B testing that might not give us all the answers, and A/B testing might limit innovation if not used correctly.  I’ll share examples from my previous examples and lessons I learned from interviewing 10+ ML eng

Daliana Liu

Senior Data Scientist @Predibase and “The Data Scientist Show" Podcast Host