Program Speakers: Larry Lancaster, Founder and CTO @Zebrium, and Gavin Cohen, VP of Marketing @Zebrium
Logs typically record the source of truth during an incident, but their sheer volume and messiness makes incident detection and root cause analysis extremely challenging. As a result, logs are typically searched reactively, relying on a mix of intuition and brute force effort. But there’s hope. Machine learning can be used to automatically detect anomalous log patterns and correlate them with root cause.
In this webinar we’ll discuss and demonstrate an approach that utilizes unsupervised machine learning to structure and categorize streaming log events and then learn normal and anomalous log patterns. The end result is reliable auto-detection of incidents and their root cause.