Machine Learning in the Wild
March 25, 2026 - Raphaël Géronimi
« All happy families are alike; each unhappy family is unhappy in its own way. »
Tolstoy's opening line may resonate when you have worked on machine learning models for a long time. Successful models tend to look alike: clean data, reliable data collection, hard work to get everything right, and enough stakeholder trust to hand over the reins in production. On the other hand, every failed model fails in its own way - a training set that silently drifted, a feature that leaked the future, a business constraint no one thought to verify, or subtle context shifts once the system went live. No two headstones read the same.