The MLOps landscape is overwhelming: 180+ projects, dozens claiming to solve "AI on Kubernetes," and most teams cobbling together tools without clear understanding or architecture. Through teaching 100,000+ engineers in various courses on CNCF, DevOps, and Machine Learning, we've discovered a power combination of tools for MLOps that works.

The biggest questions platform and ML students ask us:
- "Which CNCF projects do we actually need for each MLOps stage?"
- "Where do we start and what projects/tools do we need"
- "How do we go from experiment to production safely?"

This hands-on tutorial is an CNCF-opinionated attempt to answer these questions with a battle-tested MLOps pipeline built from thousands of student deployments, lab failures, and validation against the latest CNCF project capabilities. Leave with working manifests and a working GitHub repository showcasing cutting-edge CNCF capabilities (Volcano v1.11, OpenCost Incubating, HAMi GPU virt )for your environment.