AI/ML Workloads On Kubernetes

​AI and machine learning workloads are increasingly becoming part of modern application platforms, raising important questions about how they are built, deployed, and operated. This session explores the intersection between AI/ML workloads and Kubernetes, focusing on where Kubernetes fits within the broader landscape of ML platforms and infrastructure choices.

​Participants will gain a conceptual understanding of how containerization and orchestration concepts relate to AI/ML workflows, common architectural patterns, and the types of challenges teams consider when evaluating Kubernetes for training, inference, and data-intensive workloads. The session also touches on resource management, GPUs, scalability, and operational considerations that influence platform decisions.