Session

Hands-on with Ray on Kubernetes

The rapidly evolving landscape of Machine Learning and Large Language Models demands efficient scalable ways to run distributed workloads to train, fine-tune and serve models. Ray is an Open Source framework that simplifies distributed machine learning, and Kubernetes streamlines deployment.
In thise hands-on session we will explore Ray as a framework and how it integrates 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.we'll uncover how to combine Ray and Kubernetes for your ML projects.

Abdel Sghiouar

Cloud Developer Advocate

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