AI Quality Signals

Production AI monitoring needs both service health and model health. A fast endpoint can still be wrong, stale, or unsafe for its intended users.

Signal stack

• Service: latency, errors, throughput, saturation.
• Data: freshness, missingness, drift, schema changes.
• Model: evaluation score, sampled feedback, fallback rate.
• Governance: access, policy violations, incident status.

Drift sketch

[ \Delta = |p_{today}(x) - p_{baseline}(x)| ]

The exact drift metric can vary, but the operating question is stable: did the input distribution move enough to threaten quality?

Practical launch dashboard

Keep the dashboard small. A responder should be able to see the first likely cause in under a minute.