Ray Use Cases
How Spotify Built a Robust Ray Platform with a Frictionless Developer Experience
September 19, 1:45 PM - 2:15 PMWhat do you need to think about if you want to build a centralized Ray platform for thousands of users of diverse backgrounds and use cases? Our talk shares what Spotify's ML platform team has learned and solved over the past year. We created a seamless developer experience by making it easy to not only get computational resources but also start coding on it. We enhanced our platform's reliability, scalability, performance, and cost-efficiency. We also leveraged the Ray ecosystem for ML development to solve real business problems. These efforts have led to broader adoption of Ray in ML applications at Spotify.
We describe the goals and design decisions of our managed Ray platform, our focus on a frictionless developer experience for ML practitioners, and how Ray has accelerated various ML applications. We hope our stories and learnings will inspire and help other members of the community on their Ray journey.
About David
David Xia is a senior engineer on Spotify's ML platform team. Over the past year he helped build and operate a centralized Ray platform that enables Spotify ML practitioners to easily start prototyping their ideas and scaling their workloads. Before that he worked on Spotify's core infrastructure for backend services, specifically on deployment tooling.
About Keshi
Keshi Dai is a Staff ML Engineer on the Spotify Machine Learning platform team. He has been working on building and managing a centralized Ray platform to help ML practitioners at Spotify to drive business impact. He has also worked on applied ML for recommendations in the past, and has built models for ads optimization, top e-commerce websites, and music recommendation systems.
David Xia
Senior Machine Learning Engineer, Spotify
Keshi Dai
Staff Machine Learning Engineer, Spotify
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