The Machine Learning Canvas, run properly, on your project.
The canvas is a free 1-page tool for laying out what to predict, what decision it feeds, and what a mistake costs, before anyone builds anything.
I'm Louis Dorard. I created it in 2015, and we run it live with you, on the project you're scoping right now.
You've scoped this before. It still stalls in the same place.
Someone says "AI-driven churn prediction." Three weeks later nobody agrees what counts as churn, who acts on the score, or what a false alarm costs. The project either drags into a fourth week of workshops, or it ships something nobody asked for.
Scoping drags, and nothing ships while it does.
Someone says "AI" and means five different things. Nobody's asked which one.
The project ships. Nobody agrees whether it worked.
1 page. 10 boxes. The whole project, before anyone touches data.
The Machine Learning Canvas puts the prediction, the decision it feeds, the data behind it, and what a mistake costs, on one page — agreed before the first line of code.
What teams get with OWNML
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A common structure and vocabulary to scope, compare, and discuss ML work across teams.
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Each ML initiative starts with explicit assumptions, success criteria, and decision context—reducing overbuilding and rework.
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One-pagers and scorecards that translate ML progress into business terms, supporting prioritization and funding decisions.
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Early experiments are guided by assumptions and impact simulation, making it easier to decide whether to scale, pivot, or stop.
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Teams keep their technical freedom, while leadership gains comparability and governance across the ML portfolio.
Scales from one team's project to a department’s portfolio
I created the Machine Learning Canvas in 2015. 15,000+ practitioners have downloaded it since.
Bring the project you're scoping
20 minutes on it, and you'll know where it's likely to break.