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16-17 June, 2026
Mumbai, India
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Note: The schedule is subject to change.

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IMPORTANT NOTE: Timing of sessions and room locations are subject to change.


Wednesday June 17, 2026 12:50pm - 1:30pm IST
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.
Speakers
avatar for Akash Jaiswal

Akash Jaiswal

Software Engineer, Oracle
Software Developer @Oracle,
GSoC 2025 KubeFlow, GSoC 2022 CC Extractor
Speaker at 5+ events
avatar for Abhijeet Dhumal

Abhijeet Dhumal

Software Engineer, Red Hat
Software Engineer at Red Hat, specialising in cloud native AI and Kubernetes infrastructure.
An active open-source contributor to CNCF projects as well as OpenSource communities - Kubeflow, Ray and Feature Store with experience in cloud-native AI/ML platform development and distr... Read More →
Wednesday June 17, 2026 12:50pm - 1:30pm IST
Lotus 3 (Third Floor)
  CI/CD

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