Use AzureML registries to share ML pipelines and models across teams and workspaces
- Train a model in the “dev” workspace and deploy it to “test” and “prod” workspaces, while tracking lineage across the entire lifecycle
- Publish environments and components to Registries and use them to submit pipeline jobs to different workspaces (with full reproducibility)
- Discover and reuse training tools, data processing utilities, and pretrained models in your organization