Introduction
Weights & Biases (W&B) is a platform for the full machine learning lifecycle. It provides experiment tracking, hyperparameter logging, artifact storage, and interactive visual reports to help teams iterate and ship models faster.
Key Features
- Experiment tracking (Track): record metrics, hyperparameters and outputs.
- Reports and visualization: interactive dashboards and comparison views.
- Artifacts and dataset/model versioning for reproducibility.
Use Cases
- Tracking and reproducing training experiments during research.
- Sharing interactive reports and analyses across teams.
- Managing model versions and monitoring performance in production.
Technical Details
- SDKs for Python and integrations with major frameworks (PyTorch, TensorFlow, Hugging Face).
- Supports cloud-hosted and self-hosted deployments for different scale and privacy needs.
- Deep integration with W&B features like Tables, Reports and Weave for analysis and debugging.