Alerting and Retraining Triggers — ML Ops & Data Science in Production

Deploying a model is only half the battle — keeping it healthy over time is the real challenge. Production ML models degrade silently: the world changes, user behaviour shifts, and data pipelines introduce subtle corruptions. Alerting and retraining trigger systems are the immune system of a production ML platform. They define thresholds for acceptable performance, continuously evaluate whether the model is within those thresholds, fire alerts when thresholds are breached, and initiate retraining workflows automatically or with human approval. This lesson covers how to design those thresholds, wire them into Prometheus and Grafana, integrate with PagerDuty, and build Airflow-orchestrated retraining pipelines that promote a new model only after it beats the current champion in a rigorous comparison.

Analogy🏏Cricket

🏏 Think of it like cricket: Evidently AI is the IPL's official analytics platform — rather than each franchise building their own stats system, they use a shared platform that automatically computes every standardized metric: batting averages, economy rates, strike rates, net run rates. When Virat Kohli's performance drifts from his baseline, the platform highlights it automatically with charts. Evidently does the same for ML models: instead of each team coding their own drift detectors, they use Evidently's pre-built metrics and get standardized, comparable reports automatically. The standardization is the strategic point, not a convenience: because every franchise reads the same metric definitions, a drift score of 0.3 means the same thing in every dashboard, reports can be compared across teams and seasons, and a new analyst is productive on day one. Hand-rolled monitoring scripts fail exactly here — every team's 'drift check' quietly means something different, and nobody can audit whose alarm was right.