How do you design fault-tolerant training in Keras?

Fault-tolerant training ensures recovery from interruptions using checkpoints and resumable pipelines.

In production, training jobs may fail due to node crashes, GPU resets, or network issues. Keras supports fault tolerance via ModelCheckpoint, tf.data checkpointing, and resuming from SavedModel states. Combined with distributed strategies, training can resume exactly from the last stable epoch.