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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?โ€‹

If this is your first time using W&B we suggest you explore the following:

  1. Experience Weights & Biases in action, run an example introduction project with Google Colab.
  2. Read through the Quickstart for a quick overview of how and where to add W&B to your code.
  3. Read How does Weights & Biases work? This section provides an overview of the building blocks of W&B.
  4. 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.
  5. View the API Reference guide for technical specifications about the W&B Python Library, CLI, and Weave operations.

How does Weights & Biases work?โ€‹

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. Learn how to track dependencies and results across machine learning pipelines with our data and model versioning guide.
  6. Manage the model lifecycle from training to production with Model Management.
  7. Visualize predictions across model versions with our Data Visualization guide.
  8. Organize W&B Runs, embed and automate visualizations, describe your findings, and share updates with collaborators with Reports.
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