Integrating AI into Operations

Learning objectives

• Expose models through simple services
• Monitor performance in production
• Keep humans in the loop for risky decisions

Serve the model

A model is most useful behind a simple, well-documented API that other systems call. Keep the interface stable so teams can adopt it without coupling to the internals.

POST /predict
{ "features": { "amount": 120.50, "source": "web" } }
-> { "risk": 0.83, "label": "review" }

Monitor after launch

Models drift as the world changes. Track prediction distribution, performance indicators, and data quality continuously so degradation is noticed early.

Human oversight

For consequential decisions - credit, hiring, health - keep a human in the loop and log the model's reasoning. AI should inform decisions, not silently make them.

Key takeaways

• Serve models behind stable APIs.
• Production monitoring is mandatory, not optional.
• High-stakes decisions deserve human review.