Exploring the Ray Framework for accelerating AI/ML Applications

Are you a machine learning engineer or an enthusiast looking to scale your model training and serving processes on Kubernetes? Are you an engineer or enthusiast working with distributed systems and high-performance computing? Join us for an insightful Meetup to explore how the Ray framework and KubeRay revolutionise distributed computing for model training and serving on Kubernetes clusters.

Ray is an open-source library that seamlessly allows developers to scale their Python code—from single machines to large clusters. KubeRay extends Ray’s capabilities by integrating it with Kubernetes, enabling you to run distributed Ray applications efficiently on Kubernetes infrastructure.

Agenda

6 pm to 7 pm – Networking with Drinks and Food.

7 pm to 8 pm – Introduction to Ray and its key components (Data/Train/Serve) - by Carlos Queiroz, PhD [1]

8 pm to 9 pm – Deploying and managing Ray application on Kubernetes with KubeRay - by Chris Butler - Senior Principal Chief Architect at Red Hat [2]

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[1] https://www.linkedin.com/in/caxqueiroz/
[2] https://www.linkedin.com/in/christopherjbutler

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