![]() ![]() Register the trained model in the registry.Use the component from registry to submit a model training job in a workspace.Create an environment and component in the registry.From there, colleagues from other teams can search and reuse the assets you shared in their own experiments. In this scenario, you may want to publish a trained model and the associated components and environments used to train it to a central catalog. Share and reuse models and pipelines across different teams: Sharing and reuse improve collaboration and productivity.In this case you, want to have end-to-end lineage between endpoints to which the model is deployed in test or prod workspaces and the training job, metrics, code, data and environment that was used to train the model in the dev workspace. Cross-workspace MLOps: You're training a model in a dev workspace and need to deploy it to test and prod workspaces.There are two scenarios where you'd want to use the same set of models, components and environments in multiple workspaces: Using registries, you can share models, components, and environments. Azure Machine Learning registry enables you to collaborate across workspaces within your organization. ![]()
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