The model did not break. The upstream team renamed a column, and the model kept running — silently feeding a now-empty (or misaligned) feature into every prediction, with no error, no crash, and no obvious symptom until someone notices the metrics have quietly degraded.
Preprocessing decides more of a model's final performance than the choice of algorithm does. It also has to be treated as part of the model, not a one-off step: whatever transformation is applied to training data must travel with the model into production and be fitted only on training data, never on validation or test data (see Train/Validation/Test Splits).
A GPU can process a batch of data in milliseconds — and then sit idle for seconds waiting for the next batch to be loaded and preprocessed from disk. The input pipeline is where a surprising fraction of real training time actually goes, and getting it wrong silently turns an expensive GPU into an expensive way to wait for a CPU.