In this talk, I will look at how organizations building on Databricks, Azure, and open ML stacks can start aligning with Kubernetes‑native practices for batch workloads, observability, and governance—without needing to be cluster experts. We’ll discuss how ML pipelines (training/inference) can map into Kubernetes batch workloads. We will explore Agentic AI on top of Kubernetes for governance (drift, explainability, compliance)

Attendees will walk away with a clear mental model of how their ML infrastructure intersects Kubernetes, plus a pragmatic adoption path for leaders coming from a Data/AI background