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
- Launch: Scale and automate workloads
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:
- Learn about runs, W&B's basic unit of computation.
- Create and track machine learning experiments with Experiments.
- Discover W&B's flexible and lightweight building block for dataset and model versioning with Artifacts.
- Automate hyperparameter search and explore the space of possible models with Sweeps.
- Manage the model lifecycle from training to production with Model Registry.
- Visualize predictions across model versions with our Data Visualization guide.
- 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.