AIOps: MTTR cut by 3x and 70% fewer alerts
IASWITCH implemented alert correlation and anomaly detection to cut MTTR by 3x and remove 70% of non-actionable alerts.
In brief
- Context: alert fatigue and long incident resolution
- Solution: alert correlation + anomaly detection
- Result: MTTR /3, 70% fewer non-actionable alerts
- Stack: Prometheus, ML, SRE
The challenge
Operations teams suffered from alert fatigue: too many non-actionable notifications drowned real incidents, lengthening resolution time.
The solution
We implemented alert correlation (grouping symptoms of the same incident) and ML-based anomaly detection, rethinking alerts around SLOs rather than raw thresholds.
The results
70% of non-actionable alerts removed and MTTR cut by 3x. Teams focus on real, high-impact incidents.
Frequently asked questions
How do you cut MTTR by 3x?
By correlating alerts from the same incident, detecting anomalies and prioritizing by SLO, we speed up diagnosis and resolution — by up to 3x on this engagement.
What is alert fatigue and how do you fix it?
Alert fatigue happens when too many non-actionable notifications drown real incidents. We fix it by removing noise, correlating alerts and alerting by SLO.
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