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Use W&B Models as a central system of record for your best models, standardized and organized in a model registry across projects and teams.

Model registry featuresโ€‹

  • Versioning: Bookmark your best model versions for each machine learning task
  • Lifecycle: Move model versions through the lifecycle from staging to production
  • Lineage: Audit the history of changes to production models

How it worksโ€‹

Track and manage your trained models with a few simple steps.

  1. Log model versions: In your training script, add a couple lines of code to save the model files as an artifact to W&B.
  2. Compare performance: Check live charts to compare the metrics and sample predictions from model training and validation. Identify which model version performed the best.
  3. Link to registry: Bookmark the best model version by linking it to a registered model, either programmatically in Python or manually in the W&B UI.
  4. Test and deploy: Transition model versions through customizable workflows stages, such as staging and production.

How to get startedโ€‹

Try the Quickstart to log and link a sample model in just two minutes.

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