What happens in a project review
A project review is a free 30-minute call about a machine learning project you're scoping or running. We go through it on the Machine Learning Canvas (MLC) — one page for designing an ML system — and look for the gaps that stop projects. You leave knowing where your project is likely to break, and what to check before anyone builds it.
I draft or mark up your MLC
When you book, share your canvas draft if you've started one, or a few lines about the project if you haven't. If it's for a client, you don't need to name them.
I mark up your draft, or write one from your lines. Every line on it is either what you told me, a guess ending in (my guess), or a question that names who can answer it.
The 30 minutes
I show you the draft and tell you it's probably wrong in places. We discuss the context, what stage the project is at, who owns the business result it's meant to change, etc.
We fill in the rest of the canvas together, and I look for where it breaks.
I ask questions about your data (e.g. testing whether your history has enough examples whose outcome is already known, whether it matches what the model will actually see when it runs).
"We'd have to check" is the most common answer, and it counts as a finding. Expect 1 finding that changes the plan, and 2 questions nobody on the project can answer yet.
I ask what your predictions save and cost. For a yes/no prediction, that's what a correct prediction saves and what a false alarm costs. For a number, like a demand forecast, it's what predicting too high/low costs.
Together, those numbers decide whether acting on the predictions pays off (even when they're wrong). Most teams don't have them yet, so we make a first guess at each one together, from what you know. Every guess gets the name of who can correct it.
Those guesses are where a dollar value for the project starts.
I share my recommendations on next steps.
Then I explain how I work with teams after a review, and what it costs. Working together, I take the costs we guessed through a step-by-step method that turns them into a target for what the system is worth, the performance it has to hit to earn it, and a spec your engineers/agents can build from. We'll discuss whether it's a fit.
What you get
The same day, I send a short written follow-up you can forward to a partner who wasn't on the call. It holds your MLC as we edited it, the findings in the order you'll want to start them (what needs other people first), and my recommendations on next steps.
I do these reviews for free because reading real projects is the fastest way I have to keep the method honest, and because some of them turn into work we do together.
Book a project review
Louis Dorard
Creator of the Machine Learning Canvas