Using AI Factsheets for AI Governance

Track a machine learning model from request to production by gathering metadata and facts about the model lifecycle using AI Factsheets. Use the detailed information in the factsheets to keep stakeholders informed and to meet your governance and compliance goals. Factsheets can be shared, archived, or printed as report.

Managing governance with AI Factsheets

Before you develop and AI solution you must first define the business use case and then manage the development, testing, and deployment of the solution. You can manage and govern the flow of information by create a model use case, that defines the goals of the model. When the model is approved and development starts, track the assets in the use case, capturing all relevant data with AI Factsheets. 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

Defining use cases in a model inventory

The model inventory is a view where you can define a use case to request a new model, then track the model and related assets through its lifecycle. A typical flow might go as follows:

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 name and states the basic parameters for the requested model.
When the request is saved, a model use case is created in the inventory. Initially, the use case is in the Awaiting development state because there are no assets to accompany the request.
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.
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.
As the model advances in the lifecycle, the model use case and the AI factsheet reflect all updates, including deployments and input data assets.
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 Use cases. For real-world use cases and tutorials for using AI Factsheets to orchestrate AI solutions, see:

AI governance use case provides context for how ModelOps can mesh with AI Governance to provide a comprehensive plan for tracking machine learning assets in your organization.
AI governance use case: Build and deploy a model tutorial

Learn more

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

Next steps

Learn about creating and viewing model use cases.
For details on the templates used to structure the factsheets, see Generating reports for factsheets and model use cases.

Parent topic: Managing AI Lifecycle with ModelOps