What is the importance of maintaining an AI model catalog?

• A. To keep track of AI products/services developed internally.
• B. To store raw training data.
• C. To influence marketing strategies.
• D. To manage cloud costs.

Answer: (A)

Maintaining an AI model catalog centers on governance and lifecycle management. It creates a centralized record of the models an organization has, who owns them, what they do, their versions, deployment status, and the metadata that describes how they were created and evaluated. This enables clear model provenance, consistent risk assessment, and auditability, so teams can track how models evolve, how they were trained, and how they’re used across projects. With this catalog, you can promote reuse of existing models, avoid duplication, and enforce policies around deployment, monitoring, and decommissioning.

The catalog isn’t meant to store raw training data or datasets; data governance handles data assets and may link to datasets, but the actual data isn’t kept in the model catalog. Marketing strategies aren’t derived from this catalog, and cloud cost management is a separate concern, though good governance can indirectly inform cost decisions by showing which models are in use.