Machine learning projects often fail not from lack of technical skill but from poor process. Eugene Yan's essay outlines four concrete mechanisms to improve the odds: pilot and copilot setups, literature review, methodology review, and timeboxing. Each addresses a different failure mode, from uncertainty about whether the problem is solvable to drifting scope.
The pilot and copilot model pairs a small, focused experiment (the pilot) with a parallel, lower-stakes effort (the copilot) to compare approaches and de-risk decisions. Literature review ensures the team builds on prior work rather than reinventing it, while methodology review checks that the evaluation and modeling choices are sound before going further. Timeboxing sets a hard limit on exploration, forcing a decision point.
Yan argues these mechanisms are not one-off steps but ongoing practices that keep projects honest. They are especially useful in settings where ML work can easily become open-ended research. The essay is a reminder that effective ML delivery is as much about project design as about algorithms.