Accuracy is not enough
A model that scores well offline can still fail in production. Readiness means checking latency, monitoring, fallbacks, and data contracts before launch.
- Verify latency and throughput meet the service budget.
- Confirm the input schema matches what serving will receive.
- Define a fallback for when the model is unavailable or unsure.
Watching it live
Models decay as the world shifts. You need eyes on the system after launch.
- Monitor input drift and prediction distribution.
- Track the live metric, not just offline accuracy.
- Set alerts and a rollback path for regressions.
The gate
Each gate must pass before traffic flows.
Key idea
Production readiness goes beyond offline accuracy to verify latency, input contracts, monitoring, drift detection, and fallbacks, so the model behaves safely on real traffic.