An organization's AI governance policy requires that all AI models deployed in production must have documented model cards that include fairness metrics, known limitations, and intended use cases. Where in Microsoft's AI platform should these model cards be stored and managed to be discoverable by the security and compliance teams?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Here's the deal — a is correct because the Azure Machine Learning Model Registry is the purpose-built location for registering production AI models. Models can include detailed documentation including model cards with fairness metrics, known limitations, and intended use cases stored in model descriptions, tags, and attached README files.
Full explanation below image
Full Explanation
A is correct because the Azure Machine Learning Model Registry is the purpose-built location for registering production AI models. Models can include detailed documentation including model cards with fairness metrics, known limitations, and intended use cases stored in model descriptions, tags, and attached README files. The registry provides version control and enables the compliance team to review model documentation alongside deployment history. B is incorrect because while Purview Data Catalog can catalog models as data assets, it is primarily designed for data assets (datasets, tables) and requires additional setup to serve as a model governance catalog compared to the purpose-built Azure ML Model Registry. C is incorrect because storing model cards in DevOps repos provides version control but makes them less discoverable for compliance teams who need to review production model governance across the organization. D is incorrect because SharePoint is a generic document management system that lacks integration with the model deployment lifecycle.