What is W&B?

Weights & Biases (W&B) is the AI developer platform, with tools for training models, fine-tuning models, and leveraging foundation models.

W&B consists of three major components: Models, Weave, and Core:

W&B Models is a set of lightweight, interoperable tools for machine learning practitioners training and fine-tuning models.

• Experiments: Machine learning experiment tracking
• Sweeps: Hyperparameter tuning and model optimization
• Registry: Publish and share your ML models and datasets

W&B Weave is a lightweight toolkit for tracking and evaluating LLM applications.

W&B Core is set of powerful building blocks for tracking and visualizing data and models, and communicating results.

• Artifacts: Version assets and track lineage
• Tables: Visualize and query tabular data
• Reports: Document and collaborate on your discoveries

How does W&B work?

Read the following sections in this order if you are a first-time user of W&B and you are interested in training, tracking, and visualizing machine learning models and experiments:

1. Learn about runs, W&B's basic unit of computation.
2. Create and track machine learning experiments with Experiments.
3. Discover W&B's flexible and lightweight building block for dataset and model versioning with Artifacts.
4. Automate hyperparameter search and explore the space of possible models with Sweeps.
5. Manage the model lifecycle from training to production with Model Registry.
6. Visualize predictions across model versions with our Data Visualization guide.
7. Organize runs, embed and automate visualizations, describe your findings, and share updates with collaborators with Reports.

Are you a first-time user of W&B?

Try the quickstart to learn how to install W&B and how to add W&B to your code.