Training Machine Learning (ML) models on Kubernetes

In this episode of the Kubernetes Bytes podcast, Bhavin sits down with Bernie Wu, VP Strategic Partnerships and AI/CXL/Kubernetes Initiatives at Memverge. They discuss about how Kubernetes is the most popular platform to run AI model training and model inferencing jobs. The discussion dives into model training, talking about different phases of a DAG, and then talk about how Memverge can help users with efficient and cost-effective model checkpoints. The discussion goes into topics like saving costs by using spot instances, hot restart of training jobs, reclaiming unused GPU resources, etc. Check out our website at https://kubernetesbytes.com/ Episode Sponsor: Nethopper Learn more about KAOPS: @nethopper.io For a supported-demo: [email protected] Try the free version of KAOPS now! https://mynethopper.com/auth Cloud Native News: https://www.aquasec.com/blog/linguistic-lumberjack-understanding-cve-2024-4323-in-fluent-bit/ https://kubernetes.io/blog/2024/05/20/completing-cloud-provider-migration/ https://thenewstack.io/introducing-aks-automatic-managed-kubernetes-for-developers/ https://www.harness.io/blog/harness-to-acquire-split Show Links: https://www.linkedin.com/in/berniewu/ https://criu.org/Main_Page https://memverge.com/ https://youtu.be/tY8YOMRuqWI?si=yB3hHqLUpYPZ-KWN https://youtu.be/ND4seSKpJHI?si=shh0iuA9qC-dO6eb Timestamps: 01:04 Cloud Native News 08:47 Interview with Bernie 51:40 Key takeaways