Introduction to Distributed ML Workloads with Ray on Kubernetes

Learn how to scale machine learning workloads in this technical conference talk that explores the integration of Ray framework with Kubernetes for distributed computing. Discover fundamental Ray concepts including actors and tasks, while gaining practical knowledge on establishing a Ray cluster within Kubernetes and executing distributed machine learning training jobs. Explore how this powerful combination addresses the challenges of training and fine-tuning large language models and other ML applications in a scalable environment. Perfect for developers and ML engineers looking to enhance their understanding of distributed computing infrastructure for machine learning applications.