Checkpointing is the process of saving an AI model’s progress during training at regular intervals. These saved “checkpoints” capture the model’s current state—like its weights, optimizer settings, and training progress—so teams can pause and resume training without starting from scratch. Checkpointing is also useful for recovering from failures, comparing model versions, and experimenting with different training paths. It's a key part of building reliable, large-scale AI systems.
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