How does TensorFlow handle consistency between training checkpoints and live serving models?

Consistency is maintained using versioned checkpoints and atomic model deployment strategies.

TensorFlow checkpoints store model weights at specific training steps. In production, TensorFlow Serving loads specific versions of these checkpoints. However, inconsistency can arise if training continues while serving uses older weights. To solve this, systems use snapshot-based exports and atomic model version swaps.