Fix Predictive Alerting False Positives in Prometheus Python Models

Short summary

A practical guide to fixing false positives when deploying ML-based predictive alerting on top of Prometheus, focusing on the mismatch between training-time and inference-time data pipelines. The root cause is almost always query resolution drift, counter/rate confusion, staleness handling, or cardinality explosion — not the model threshold. The article provides concrete Python code fixes including pinning step intervals, using rate() in PromQL, feature contract verification, and cardinality guards.

• •False positives in production ML alerting stem from training/inference query mismatches, not model thresholds
• •Four key failure modes: step drift, counter vs rate confusion, NaN staleness, and cardinality explosion
• •Concrete fixes include pinning query resolution, wrapping counters in rate(), feature hash verification, and cardinality guards

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