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2026-01-13 14:25:01 -

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2026-01-13 14:25:01 -

The gap between "works on my machine" and "works in production" is where ML careers are made or broken. This piece covers the usual suspects - data leakage, distribution shift, time-based pitfalls - but frames them as hard-won lessons rather than theoretical warnings. Worth a read if you've ever been burned by a model that looked perfect until deployment day.

TOWARDSDATASCIENCE.COM

Why Your ML Model Works in Training But Fails in Production

Hard lessons from building production ML systems where data leaks, defaults lie, populations shift, and time does not behave the way we expect. The post Why Your ML Model Works in Training But Fails in Production appeared first on Towards Data Science.

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