Scoping Is Now the Bottleneck in ML Projects, Says Louis Dorard
louisdorard · x · 2026-10-29
- The author argues scoping—not building—is now the bottleneck in ML projects: in 2015 it felt optional, but AI has made build and deploy cheap while defining the problem remains the narrow part.
- Quick test: state your last ML project's yearly value in one sentence; if it takes over a minute, scoping is your bottleneck.
- Choosing well requires three answers: which problem, for whom; how much it's worth; how hard it is to build. His Machine Learning Canvas puts all three on one page (value proposition, impact simulation, data sources, features, etc.).
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