Why Your Machine Learning Model Performs Well but Fails in Production

Machine learning models can achieve high accuracy during development but fail in production due to various factors such as data drift, temporal distribution shift, and concept drift. This matters because real-world conditions often differ from training environments, leading to performance degradation. Developers must ensure consistent preprocessing and robust system design to mitigate these issues.

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