Use W&B Artifacts for dataset versioning, model versioning, and tracking dependencies and 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 like S3, GCP, or your own system.