Real-world uses

Created for this library

1. 1.

   An ML platform team uses a checkpoint Saver to write model weights every 1,000 steps so training can resume from the latest checkpoint after a failure.

2. 2.

   A research team uses a Saver to keep the top three checkpoints by validation metric for later promotion.

3. 3.

   An ML engineer uses a Saver to archive model checkpoints across versions for backward-compatibility testing.