Model Management
Manage the model lifecycle from training to production
Use the W&B Model Registry as a central system of record for models.
- Create Registered Models to organize your best model versions for a given task
- Track a model moving into staging and production
- See a history of all changes, including who moved a model to production
- 1.
- 2.art = wandb.Artifact("my-object-detector", type="model")art.add_file("saved_model_weights.pt")wandb.log_artifact(art)
- 3.From the Artifact page, link the artifact version to the registry.
Iterate to get the best model version for a task, and catalog all the changes along the way.
- Track every model version in a central repository
- Browse and compare model versions
- Capture training metrics and hyperparameters
Document and reproduce the complete pipeline of model training and evaluation.
- Identify the exact dataset version the model trained on
- Restore the training code, including git commit and diff patch
- Get back to the model’s hyperparameters and other metadata for reproducibility
- Dig in to upstream jobs that can affect model performance
Manage the process as a model moves from training through staging to production.
- Highlight the best model versions that are being evaluated for production
- Communicate where a model version is in the process — staging, production etc
- Review the history of model versions that moved through each stage
This new feature is now turned on for all users to try for free, up to:
- 5 Registered Models, with unlimited versions linked to each model
- 10 most recent steps of Action History shown in the UI for each registered model
Dig into the details of how to use Weights & Biases for model management:
Last modified 3mo ago