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.
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