Accelerating ML Research and Prototyping with Ray
Spotify started evaluating Ray on Kubernetes as a distributed compute platform that enables ML research and experimentation for ML workflows in early 2022. Ray and its ecosystem provide researchers and data scientists at Spotify with a better model development experience, instant access to distributed computing, and an expressive programming interface. These features greatly complement our existing production ML workflow.
In this talk, we share the story of how Ray started at Spotify, what our long-term goals are with Ray, and how Ray enabled tangible business impact for Spotify by accelerating an ML research use case for improving podcast recommendations.
Supporting Model Serving at Scale: From Backend to On Device
Recently our ML serving team expanded its scope from backend infra to on-device models. The talk covers some (early) observations on this project and the new challenges that came with it.
