Introduction to Distributed ML Workloads with Ray on Kubernetes

Explore the integration of Ray and Kubernetes for efficient distributed machine learning in this 26-minute conference talk. Discover basic Ray concepts like actors and tasks, and their application to ML workflows. Learn how to set up a simple Ray cluster within Kubernetes and execute your first distributed ML training job. Gain insights into scaling machine learning and large language model workloads for training, fine-tuning, and serving models using these powerful open-source tools.