How do TensorFlow systems fail due to incorrect data preprocessing parity between training and inference?

Failure occurs when training and inference pipelines apply different transformations to the same features.

One of the most critical production ML failures is training-serving skew. During training, data may be normalized, encoded, or imputed in one way, while inference uses slightly different logic. TensorFlow models assume identical input distributions. Even small mismatches (scaling, missing feature handling, category encoding) cause major degradation without runtime errors, making this a silent but severe failure mode.