Why do TensorFlow pipelines break when feature stores become inconsistent?

Feature store inconsistencies cause training-serving mismatch and degraded model performance.

Feature stores centralize feature computation for ML systems. If offline and online feature stores diverge due to latency, missing updates, or schema mismatch, models receive inconsistent inputs. TensorFlow models assume feature parity between training and inference, so inconsistency leads to silent degradation.