A quick and to-the-point demonstration of Zebrium’s log anomaly detection and root cause analysis platform that is built using unsupervised machine learning.

For this demo, we set up a Kubernetes cluster with Google cloud microservice cloud-native app.
And then we broke the app by scaling down one of the services.

While this was happening, Zebrium was collecting logs from this environment. As soon as the problem occurred, Zebirum’s machine that generated a root cause report showing details of the problem and root cause. The technology works by using ML to automatically structure logs, perform anomaly detection on the logs and then identifying correlated anomalies across the logs. It works with streaming logs or log files.

Here’s a step-by-step guide that shows you how to do this yourself.: [ Ссылка ]

Sign-up for a free trial here: [ Ссылка ]

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