A Zebrium Whitepaper - Using ML on logs

Historically, logs have been collected and used mostly for reactive troubleshooting of issues found by other monitoring and alerting tools. This is because even though logs typically record the source of truth during an incident, they’re too vast and noisy to easily be used for incident alerting.

Fortunately, new approaches that use machine learning are being developed to solve this problem. They automatically detect log patterns to catch incidents and correlate them with the root cause. These approaches are built to work at scale and can finally turn our logs into a more pro-active monitoring solution.