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Machine Learning for Logs

ML for Logs Automatically Shows you the Root Cause

Zebrium - how it works-1

Logs go In, Root Cause Come Outfour simple steps-3

Step 1 - Ingest and Categorization

Start by installing one of our supported log collectors.  No parsers, code changes, rules or special log formats are needed. Then let our Machine Learning (ML) take over!

Within minutes, our ML learns the structures of your logs,  and categorizes each event into a “dictionary” of unique event types. Categorization is crucial for accurate pattern learning of your logs.

Log and metrics collector setup

Zebrium machine learning

Step 2 - Pattern and Anomaly Detection

Within the first hour, the patterns of each type of log event is learnt (and the learning continues to improve with more data). 

Log event pattern changes are scored as to how "anomalous" they are, but these anomalies tend to be very noisy. So, in order to separate signal from noise, the ML then looks for hotspots of abnormally correlated anomalies across the different log streams. 

2 - Zebrium Autonomous Incident

Step 4 - Root cause reports

The hotspots detected above are packaged into concise root cause reports that contain root cause indicators and symptoms found in the logs. 

A plain language summary of the report is also created using Generative AI. 

The entire process is completely autonomous - without requiring manual configuration,  training or large data sets.

Getting started is free and easy

Spend just two minutes of your time and you'll be amazed at what we detect!

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