How do we version and manage model weights?
Managing weight versions is key to reproducibility.
How It Works:
Use artifact stores (like S3 or MLflow) to tag weight files with metadata (training data, hyperparameters) and link them to model IDs in your registry.
Key Benefits:
- Traceability: Know exactly which weights produced which results.
- Rollbacks: Revert to prior weight versions instantly.
- Collaboration: Teams share and reproduce experiments.
Real-World Use Cases:
- A/B testing: Compare performance of weight versions in production.
- Compliance: Archive weights for audit trails.
FAQs
How store large checkpoints?
How associate metadata?
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