Why Your ML Model Works in Training But Fails in Production

Machine learning models often fail in production not because of poor algorithms, but due to incorrect assumptions about data and time. One critical error is "time travel," where a model is trained with information that wouldn't have existed at the moment of a real-world prediction, giving it an unrealistic advantage. Another subtle issue arises when default values for missing data become unintended signals, causing the model to misinterpret new or incomplete information. Ultimately, performance can degrade even when feature distributions appear stable, as shifts in the underlying user population change the meaning of the data without triggering standard monitoring alerts.