Weights & Biases

What is Weights & Biases?

Weights & Biases (W&B) is a platform for machine learning development. It provides a suite of tools that make ML projects more productive and efficient, serving as a central hub for experiment tracking, model management, and workflow automation.

Its core features include:

• Experiments – tracking of ML experiments
• Models Registry – keeps all your models organized in one place
• Launch – sets up automated ML workflows
• Sweeps – hyperparameter optimization

W&B also offers specialized tools for large language models (LLMs). W&B Prompts helps you monitor LLM pipelines and fine-tune prompts, while Monitoring tracks how your LLM APIs are used and how well they perform.

W&B Core itself includes Artifacts for versioning datasets and models, Tables for interactive data views, Reports for shareable dashboards, and Weave, an interactive app builder for ML applications.

Who created Weights & Biases?

Lukas Biewald is the founder of Weights & Biases. The company focuses on building tools that help data scientists and machine learning engineers track, visualize, and optimize their models more efficiently.

What is Weights & Biases used for?

W&B is used to:

• Track experiments and manage models with Experiments and the Models Registry
• Automate ML workflows with Launch
• Optimize hyperparameters with Sweeps
• Monitor LLM pipelines and improve prompts with W&B Prompts
• Track LLM API usage and performance with Monitoring
• Version datasets and models (Artifacts), explore data interactively (Tables), build shared dashboards (Reports), and build custom ML apps (Weave)
• Access a Resource Library, Blog & Tutorials, and enterprise MLOps services

Who is Weights & Biases for?

W&B is built for anyone working with machine learning, including machine learning professionals, data scientists, software developers, data engineers, researchers, and enterprise MLOps teams. It is used across industries such as autonomous vehicles and financial services, and supports use cases including LLMs, computer vision, and time series analysis.