ML fails silently
A broken model still returns numbers. Without monitoring, quality erodes invisibly while uptime looks perfect.
What to monitor
- Operational latency, error rate, throughput, like any service
- Data quality missing features, schema changes, range violations
- Drift input distribution shifting away from training data
- Prediction score distribution, class balance, confidence
- Outcome the actual business metric, the ground truth signal
Delayed labels
Ground truth often arrives late. Until it does, watch proxy signals like input drift and prediction distribution to catch problems early.
Alerting discipline
- Set thresholds that catch real problems without crying wolf
- Route alerts to an owner who can act
- Pair every alert with a runbook
Key idea
Monitor data, predictions, and outcomes, not just uptime, so silent model degradation turns into a clear, actionable alert.