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ML-MODEL-MONITORING5 MIN READ

Monitor symptoms before explanations

Separate user-visible model symptoms from internal causes when designing alerts.

The move: make the first alert answer "what is broken?" before it guesses "why." Symptom signals Symptoms are visible in the service outcome: bad predictions, slow decisions, failed inferences, customer complaints, manual overrides, or a product KPI crossing a guardrail. These are the strongest candidates for paging because they describe active harm. Cause signals Causes are internal explanations: a feature went missing, a join changed, a distribution shifted, a model server saturated, or a batch job lagged. These signals matter, but most belong in tickets or diagnostic views unless they reliably predict imminent harm. The paired view The best dashboard…

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