Alert Classification Engine
Auto-classify alerts as actionable or noise.
The problem
Most alerting systems treat every signal the same. A disk warning, a customer-impacting outage, and a one-off blip all flow through the same pipeline.
Without classification, on-call time is wasted on triage that the system could do automatically. The first 90 seconds of every page is often "is this real?".
Classification engines bucket alerts into actionable, informational, suppressed, or escalated before they reach a human.
Rule-based classification
Start with hand-written rules. Tag alerts by service, severity, and customer impact. Route SEV1 to PagerDuty, SEV3 to Slack, SEV4 to a daily digest.
PagerDuty event orchestration, Opsgenie integrations, and incident.io workflows all support rule trees up to 4 or 5 levels deep.
Rule-based classification handles 80% of cases. The cost is low, the maintenance burden is moderate, and the behaviour is predictable.
ML-based classification
Vendors like Moogsoft, BigPanda, and Nova AI Ops use ML to cluster, dedupe, and score alerts. Useful when alert volume exceeds 10k per day.
ML models need training data. Without 6 months of labelled history, the system cannot distinguish noise from signal. Don't enable it on day one.
Black-box scoring breaks trust quickly. Pick a vendor that explains why an alert was suppressed, not just that it was.
The feedback loop
On-call should be able to mark an alert as "this was noise" or "this was real" with one click. The classifier learns from this signal.
Without feedback, the engine drifts. Suppressed alerts that turn out to be real never feed back, and the model degrades silently.
Audit weekly. Pull a sample of 20 suppressed alerts and confirm they were genuinely noise. Mistakes here are how outages slip through.
Pick by scale
Under 1k alerts per day: rule-based via PagerDuty event rules is enough.
1k to 10k alerts per day: rule-based plus dedup and grouping. Tune with monthly audits.
Above 10k: an ML-backed classification engine pays for itself, but only if the feedback loop is wired and audited.