Tuesday, August 23
2:15 PM - 2:45 PM
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.
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.
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