Why do TensorFlow models produce correct offline metrics but fail in production metrics?

This happens due to mismatch between offline evaluation data and real-time production distribution.

Offline metrics are computed on curated datasets that are often clean, balanced, and static. In production, data is noisy, delayed, skewed, and continuously evolving. TensorFlow models evaluated offline assume i.i.d. data, but production violates this assumption due to temporal drift, missing features, and feedback loops. This leads to metric divergence between training validation and real-world performance.