Zebrium Blog

Is Autonomous monitoring the anomaly detection you actually wanted?

Automatically Spot Critical Incidents and Show Me Root Cause

That's what I wanted from a tool when I first heard of anomaly detection. I wanted it to do this based only on the logs and metrics it ingests, and alert me right away, with all this context baked in...

Automatically Spot Critical Incidents and Show Me Root Cause

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Anomaly Detection as a foundation of Autonomous Monitoring

We believe the future of monitoring, especially for platforms like Kubernetes, is truly autonomous. Cloud native applications are increasingly distributed, evolving faster and failing in new ways, making it harder to monitor, troubleshoot and resolve incidents. Traditional approaches such as dashboards, carefully tuned alert rules and searches through logs are reactive and time intensive, hurting productivity, the user experience and MTTR. 

We believe the future of monitoring, especially for platforms like Kubernetes, is truly autonomous. Cloud native applications are increasingly distributed, evolving faster and failing in new ways, making it harder to monitor, troubleshoot and resolve incidents. Traditional approaches such as dashboards, carefully tuned alert rules and searches through logs are reactive and time intensive, hurting productivity, the user experience and MTTR. We believe machine learning can do much better – detecting anomalous patterns automatically, creating highly diagnostic incident alerts and shortening time to resolution.

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