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.
Explore the basics of Artifacts for dataset versioning and model management. with a quick, interactive notebook hosted in Google Colab.
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.
Here's an example artifact where you can see the summary view of the DAG, as well as the zoomed-out view of every execution of each step and every artifact version.
To learn how to use Artifacts, check out the Artifacts API Docs →
Follow along with our interactive tutorial and learn how to track your machine learning pipeline with W&B Artifacts.