Course

Measuring & Governing AI Products

You shipped an AI feature. Now the hard part: knowing whether it's actually working, and keeping it safe as it grows. This is the product manager's toolkit for the 'after you launch' half — tie every model number to a business outcome, set the usefulness bar across quality, latency and cost, catch regressions with evals, put a control on every risk, and build the org that can scale it. Six short lessons, every screen drawn, no hype and no magic numbers.

6 lessonsIntermediate42 min

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Course lessons

Business metrics first

Model accuracy is a number, not a result. Learn to trace every model gain up to the outcome that actually matters.

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The usefulness threshold

'Best model' is the wrong question. The right one: what's the cheapest, fastest model that's still good enough?

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Evals as a product concern

A great demo isn't proof. A test set you run on every change is how you know it didn't quietly get worse.

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AI governance & risk

Governance isn't a document you file. It's a named owner and a concrete control for every way the product can go wrong.

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Scaling AI products & the AI-ready org

A pilot that works doesn't mean an org that can scale. Readiness rests on three legs — and the weakest one caps you.

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The governing loop

The whole course in one habit: tie everything to outcomes, and put an owner and a control on every risk.

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Learn it properly — tracked & personalized

Follow the whole course as a guided journey — sequential lessons, progress, streaks and a personalized path, all in the app.

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