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.

Set up W&B in 5 minutes, then quickly iterate on your machine learning pipeline with the confidence that your models and data are tracked and versioned in a reliable system of record.

This diagram outlines the relationship between W&B products.

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

• Experiments: Machine learning experiment tracking
• Model Registry: Manage production models centrally
• Launch: Scale and automate workloads
• Sweeps: Hyperparameter tuning and model optimization

W&B Prompts is for debugging and evaluating LLMs.

W&B Core is a core 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

Are you a first-time user of W&B?​

Start exploring W&B with these resources:

1. Intro Notebook: Run quick sample code to track experiments in 5 minutes
2. Quickstart: Read a quick overview of how and where to add W&B to your code
3. Explore our Integrations guide and our W&B Easy Integration YouTube playlist for information on how to integrate W&B with your preferred machine learning framework.
4. View the API Reference guide for technical specifications about the W&B Python Library, CLI, and Query Language operations.

How does W&B work?​

We recommend you read the following sections in this order if you are a first-time user of W&B:

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 Management.
6. Visualize predictions across model versions with our Data Visualization guide.
7. Organize W&B Runs, embed and automate visualizations, describe your findings, and share updates with collaborators with Reports.

Final Words