Models

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 features

• 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 works

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 started

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