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Teaching · the practice

Teaching AI in production.

I teach engineers how to take AI from prototype to production, architecture, evaluation, reliability, and the craft of shipping real systems. No slideware: every session works on real problems, real tools, real data.

01 · Currently teaching

Current course

École Polytechnique
École Polytechnique
Graduate · IP Paris
2024 – Now

“AI in Production”

A graduate course on shipping machine-learning systems: from architecture and prompt design to evaluation, monitoring, reliability, and the operational craft of running models in production.

What we cover
Architecture: agents vs. workflows, retrieval, orchestration
Prompt & context engineering for reliable behavior
Evaluation: building eval sets, measuring quality, regression
Reliability: monitoring, guardrails, fallback, cost control
From prototype to production: deployment and operations
02 · Approach

Teaching philosophy

Theory grounded in practice. I believe the fastest way to understand a system is to build it, break it, and ship it. Each session pairs clear frameworks with hands-on work on the students’ own challenges, so the concepts stick because they were used, not just heard.

01

Practical, not theoretical

Real challenges, real tools, real data, every session produces something you can use.

02

Clear frameworks

Mental models and decision frameworks that transfer beyond any single tool.

03

From beginner to expert

The level adapts: whether you’ve never shipped a model or run them daily.

03 · Resources

Course resources

Materials are shared with enrolled students. For access, talks, or guest lectures, get in touch.

Want a session for your team?

I run tailored workshops and guest lectures on production AI for companies and schools.

Get in touchAbout me