Integrated Azure ML data

After the synchronization, the assets are available in the specified domain.

Warning Do not move the assets to another domain. Doing so may lead to errors during future synchronizations.

By default, the assets are shown in a plain list, but you can enable a multi-path hierarchy to show it in a tree structure.

The Default Asset Status field in the capability determines the status of synchronized assets.

  • If you select No Status, newly created assets receive the first status listed in your Operating Model statuses, and existing assets keep their assigned status.
  • If you select Implemented, all assets receive the "Implemented" status.
Note For more information about AI Model Version asset statuses, go to Configure the lifecycle stages of AI Governance assets.

Synchronized metadata per Azure ML asset type

This table shows the metadata for the Azure ML asset type. If you do not see any of the listed synchronized metadata, you can add characteristics to the layout on the asset type page.

  • Each Azure ML model is imported as one Azure AI Model Version asset. Only the latest version of each registered model is imported.
  • The asset's name and display name are both the Azure ML model name.
  • Models created as AutoML child runs are not imported, and no error is shown for them.
  • Model Accuracy appears only when the model's latest training job reports an accuracy metric. Models without a training job are imported without metric attributes.
  • Initiating User in Source appears only when Azure ML provides the creator of the model version.

Note If you remove a label in Microsoft Azure AI after a Azure ML synchronization, the attribute for the removed label is removed when you integrate Azure ML again.

Asset type Synchronized metadata Public ID
Azure AI Model Version Description from source system DescriptionFromSourceSystem
Initiating User in Source InitiatingUserInSource
Model Accuracy ModelAccuracy
Version Version
Any custom metrics defined via the configuration, for example, Model Precision, Mean Squared Error, or Mean Absolute Error. If you use custom metrics, ensure that you add them to the assignment and layout on the asset type page.