Modern machine learning workloads demand scalable, flexible infrastructure that can handle complex computational requirements. This talk explores how Ray, an open-source unified framework, makes distributed machine learning on Kubernetes easier with its advanced capabilities.

In this talk we will explore Ray Integration with Kubernetes to run scalable distributed machine learning workloads. We will cover Ray scalability, patterns for running RayJobs and RayServe and will cover best practices for creating multi-tenant ML platforms using Ray on Kubernetes with fair-sharing of scarce hardware accelerators.