Open Source Summit India 2026

ML platforms are powerful, but not always easy to use. A data scientist might understand their model well, yet struggle with Kubernetes configs, SDK APIs, or GPU scheduling. The result is friction — and a lot of “Can someone submit this job for me?” messages.

In this talk, I’ll introduce Kubeflow MCP Server — a Model Context Protocol bridge that exposes the Kubeflow SDK as AI-callable tools. Instead of writing Python or YAML, users can train and manage workloads through natural conversation, while the MCP layer handles validation and policy enforcement underneath.

KEP: https://github.com/kubeflow/community/pull/937

We’ll show:
Turning Kubeflow operations into structured MCP tools
Pre-flight checks that catch resource mismatches before submission
Persona-based filtering so data scientists get safe access while admins keep full control
A two-phase confirmation pattern to avoid accidental large GPU allocations

This session explores what it means for ML infrastructure to become agent-friendly — without compromising governance or cluster safety.