What is Weights & Biases?
Weights & Biases is the machine learning platform for developers to build better models faster. Use W&B's lightweight, interoperable tools to quickly track experiments, version and iterate on datasets, evaluate model performance, reproduce models, visualize results and spot regressions, and share findings with colleagues. Set up W&B in 5 minutes, then quickly iterate on your machine learning pipeline with the confidence that your datasets and models are tracked and versioned in a reliable system of record.
Are you a first-time user of W&B?
If this is your first time using W&B we suggest you explore the following:
- Experience Weights & Biases in action, run an example introduction project with Google Colab.
- Read through the Quickstart for a quick overview of how and where to add W&B to your code.
- Read How does Weights & Biases work? This section provides an overview of the building blocks of W&B.
- 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.
- View the API Reference guide for technical specifications about the W&B Python Library, CLI, and Weave operations.
How does Weights & Biases work?
We recommend you read the following sections in this order if you are a first-time user of W&B:
- 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.
- Learn how to track dependencies and results across machine learning pipelines with our data and model versioning guide.
- Manage the model lifecycle from training to production with Model Management.
- Visualize predictions across model versions with our Data Visualization guide.
- Organize W&B Runs, embed and automate visualizations, describe your findings, and share updates with collaborators with Reports.