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Using AI Factsheets for AI Governance

Using AI Factsheets for AI Governance

Use a model inventory and AI Factsheets as part of your AI Governance strategy to track the lifecycles of machine learning models from training to production. View factsheets for model assets that track lineage events and facilitate efficient ModelOps governance.

Managing governance with AI Factsheets

AI Factsheets provides the capabilities for you to track data science models across the organization. View at a glance which models are in production and which need development or validation. Use the governance features to establish processes to manage the communication flow from data scientists to ModelOps administrators.

Note: Only the models that you add to use cases are tracked with AI Factsheets. You can control which models to track for an organization without tracking samples and other models that are not significant to the organization.

Tracking models in a model inventory

Tracking models in a model inventory

The model inventory is a view where you can request a new model, then track it through its lifecycle. A typical flow might go as follows:

  1. A business user identifies a need for a machine learning model and creates a model use case to request a new model. The business owner assigns a potential name and states the basic parameters for the requested model.
  2. When the request is saved, a model use case is created in the inventory, and the tracking begins. Initially, the use case is in the Awaiting development state because there are no assets to accompany the request.
  3. When a data scientist creates a model for the business case, they track the model from the model details page of the project or space, and associate it with the model use case.
  4. The model use case in the inventory can now be moved to an In progress state and stakeholders can review the assets for the use case, which now include the model.
  5. As the model advances in the lifecycle, the model use case and the AI factsheet reflect all updates, including deployments and input data assets.
  6. Validators and other stakeholders can review model use cases to ensure compliance with corporate protocols and to view and certify model progress from development to production.

Use cases and tutorials

AI Factsheets is part of IBM's data fabric collection of tools and capabilities for managing and automating your data and AI lifecycle. For details on how data fabric can support your governance goals in practical ways, see Data fabric solution overview. For real-world use cases and tutorials for using AI Factsheets to orchestrate AI solutions, see:

Learn more

Find out about working with a model inventory programmatically, with the IBM_AIGOV_FACTS_CLIENT documentation.

Next steps

Parent topic: Managing AI Lifecycle with ModelOps

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