How do TensorFlow systems maintain consistency between real-time and batch feature computation?

They use unified feature definitions and shared transformation pipelines to ensure parity.

Batch training features are computed on historical data, while real-time inference features are computed on live streams. Any mismatch in logic, timing, or aggregation windows causes training-serving skew. TensorFlow ecosystems solve this using shared feature definitions (e.g., feature stores) and consistent transformation pipelines to ensure identical logic execution.