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Registering models for tracking with AI Factsheets

Registering models for tracking with AI Factsheets

You can track models from a project, space, or catalog so that the details for the model lifecycle are displayed in a model use case, which is stored in a model inventory. After you enable model tracking, details for the model are recorded in the associated factsheet as part of your AI Governance strategy.

Tracking a model

When a model is tracked, the AI Factsheets service creates a relationship between the model and the associated model use case. All the facts about how the model was created and where the model is on the path to production are recorded in the use case for ease of review and governance.

Tracking a model from the model details page of a project, space, or catalog

  1. Click a model name to view the model details in a project, space, or catalog.

  2. Click Track this model.

  3. Choose to associate the model with an existing model use case or create a new use case.

  4. When you save the tracking information, a link to the model inventory is added to the model details page. Click the link to open the model use case in the inventory.

  5. Optionally update the model use case. For example, add tags, supporting documentation, or other details.

When you view the model detail page, tracking information is displayed. Tracking details for a model

Registering a model in a notebook

If your model is in a notebook, you can insert code to register the model with a model use case.

  1. Create a model use case:
model_usecase_catalog_id = "b54179b3-d6f9-4886-bd48-a248c60d1f36" # Specify the catalog to store the use case
model_usecase_name="model_usecase_name_here"
model_usecase_desc="model usecase for data here"

model_usecase = facts_client.assets.create_model_usecase(catalog_id=model_usecase_catalog_id,name=model_usecase_name,description=model_usecase_desc)
model_usecase.get_info(True)
  1. Create an approach in the use case:
name_approach = model_usecase.create_approach(name="nameapproach",description="approach for data here")
name_approach.get_info()
  1. Retrieve the approach:
retrieve_approaches = model_usecase.get_approaches()
name_approach = model_usecase.get_approach(approach_id=retrieve_approaches[0].get_id())
  1. Track the model in the use case:
wml_model = facts_client.assets.get_model(model_id="your model id ",container_type="project",container_id="container id")## model id is the auto ai model id
wml_model.track(model_usecase=model_usecase,approach=name_approach,version_number="major",version_comment="name model major version")

For details on using Python commands to interact with the model inventory, see Python client documentation and the IBM_AIGOV_FACTS_CLIENT documentation.

Removing a model from a use case (untracking)

Click Untrack to disassociate the model from the model use case.

Learn more

Learn about viewing model use cases and factsheets.

Parent topic: Managing an inventory of model assets.

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