Du bloc-notes à la production : pourquoi le MLOps est le vrai métier

Most introductory AI courses stop at the moment a model hits an accuracy number in a notebook. That is the easy part. The work that gets paid for — and that employers actually hire for — begins afterwards: packaging the model, monitoring it in production, rolling it back when it drifts, and keeping it cheap enough to run forever. This is MLOps, and it is a discipline, not a weekend project.

What 'production' really means

A production AI system is a service. It has uptime requirements, latency budgets, cost ceilings, and a human who is paged when it degrades. The model is one component. Around it sits containerization, a CI/CD pipeline that re-trains on new data, monitoring for input drift, and a rollback path. None of that is visible in a Kaggle score.

If your model can only run on your laptop, it does not exist as far as a business is concerned.

How the program builds this muscle

The curriculum treats deployment as a first-class skill rather than a footnote. In Session 4, students deploy models to cloud infrastructure and learn container basics. In Session 5, the production-launch course turns that into a repeatable checklist — packaging, monitoring, and a readiness gate before anything ships.

• Cloud computing (course 4.3): AWS, Azure, Google Cloud fundamentals and containerization with Docker.
• Preparing the AI solution for production launch (course 5.1): packaging models, CI/CD for ML, monitoring, and a production-readiness checklist.
• Script programming and task automation (course 4.1): the glue that turns one-off scripts into scheduled, observable workflows.

The proof is the capstone

The program ends with a supervised capstone (course 5.5) where students build a real agentic-AI business solution end to end and defend it to a panel. A capstone that has never been deployed is not a capstone — it is a homework assignment. Graduates leave able to point at a running system and say 'I built that, and here is how it stays up.'