Ray Deep Dives

Multi-model composition with Ray Serve deployment graphs

Ray Summit 2022

In this talk, we aim to show how to leverage the programmable and general-purpose distributed computing ability of Ray to facilitate the authoring, orchestrating, scaling, and deployment of complex serving graphs as a DAG under one set of APIs, like a microservice. Learn how you can program multiple models dynamically on your laptop as if you're writing a local Python script, deploy to production at scale, and upgrade individually.

About Simon

Simon Mo is a software engineer working on Ray Serve at Anyscale. Before Anyscale, he was a student at UC Berkeley participating in research at the RISELab. He focuses on studying and building systems for machine learning, in particular, how to make ML model serving systems more efficient, ergonomic, and scalable.

Simon Mo

Software Engineer, Anyscale
Ray Summit 2022 horizontal logo

Ready to Register?

Come connect with the global community of thinkers and disruptors who are building and deploying the next generation of AI and ML applications.

Save your spot

Join the Conversation

Ready to get involved in the Ray community before the conference? Ask a question in the forums. Open a pull request. Or share why you’re excited with the hashtag #RaySummit on Twitter.