Kubernetes for Machine Learning - Managing and Scaling AI Workloads

Learn to harness Kubernetes for machine learning workloads in this 42-minute conference talk from DevConf.IN 2026. Explore the powerful intersection of Kubernetes and machine learning to create data-driven applications, with speaker Drishti Jain guiding you through best practices for managing and scaling AI workloads. Discover the end-to-end ML lifecycle using Kubeflow, examine specific use cases for Kubernetes in machine learning environments, and understand common challenges along with their solutions. Dive deep into advanced Kubernetes features including horizontal and vertical scaling strategies, pod affinity and anti-affinity configurations, and specialized resource management for GPUs and TPUs. Gain practical insights into deploying, managing, and scaling machine learning applications in production Kubernetes environments.