Versioned data, models and results across your pipelines


Use W&B Artifacts to store and keep track of datasets, models, and evaluation results across machine learning pipelines. Think of an artifact as a versioned folder of data. You can store entire datasets directly in artifacts, or use artifact references to point to data in other systems.

How it works

Using our Artifacts API, you can log artifacts as outputs of W&B runs, or use artifacts as input to runs.

Since a run can use another run’s output artifact as input, artifacts and runs together form a directed graph. You don’t need to define pipelines ahead of time. Just use and log artifacts, and we’ll stitch everything together.

To learn how to use Artifacts, check out the Artifacts API Docs →