DevOps to MLOps in 2026: One Clear Path That Gets Interviews

The problem most engineers hit

You already know Docker, CI/CD, maybe Kubernetes. You open YouTube for "MLOps" and get 40 random playlists. Six months later you still cannot explain a real pipeline in an interview.

That is not a talent problem. It is a path problem.

What companies actually ask

In 2026, hiring managers for MLOps / LLMOps / AI platform roles ask for proof you can:

1. Package and deploy a model (or an LLM app) like a production service
2. Track experiments and versions (MLflow or equivalent)
3. Monitor drift, cost, and failures — not just "it works on my laptop"
4. Wire agents / RAG with guardrails when GenAI is in the stack

If you can walk through those on a whiteboard and in a repo, you are ahead of most applicants.

A clean order (do this, not everything)

Month mindset, not weekend binge.

1. Foundations you may already have — Linux, Git, Docker, basic Python, one cloud.
2. MLOps core — training → registry → deploy → monitor. One end-to-end lab beats ten theory videos.
3. LLMOps — RAG, evals, prompt/version discipline, serving.
4. AIOps + agents — alerts that mean something; agents that call tools safely (MCP, approvals).
5. Portfolio — 2–4 capstones you can demo line by line.

Skip: collecting 50 certificates with zero deploy story.

Why live teaching still wins

Recordings help. Live Q&A + someone who has run enterprise systems saves months of wrong turns. That is what I teach: production habits, not slide decks.

Start here on this site

• Full module list: Course syllabus
• Flagship program: MLOps masterclass
• Local pricing and payment: Enroll
• Questions before you pay: WhatsApp

I reply personally. Tell me your current role and target role — I will tell you if the masterclass, the automation course, or mentorship fits first.