Which activities are involved in AI operationalization?

• A. Testing, deployment, and solar integration.
• B. Training models, validating data, and deploying AI into production workflows.
• C. Model licensing and decommissioning.
• D. Data visualization and reporting only.

Answer: (B)

Operationalizing AI means taking a model from development into live use and maintaining it within production systems. It involves building the capability, ensuring the data going into the model is valid, and integrating the model so it can run in real production workflows and deliver predictions to users or downstream processes. The option that lists training models, validating data, and deploying AI into production workflows best captures this end-to-end lifecycle—creating the model, validating inputs, and placing it into production where it can be monitored and used.

The other choices miss key parts of operationalization: solar integration has no relevance to AI operations; licensing and decommissioning sit more with governance and lifecycle retirement than day-to-day production use; data visualization and reporting alone address presenting results rather than deploying and maintaining models in production.