This is a high level overview of the architecture for log anomaly detector which consists of three components.

Machine Learning (LAD-Core)

Contains custom code to train model and predict if a log line is an anomaly. We are currently using W2V (word 2 vec) and SOM (self organizing map) with unsupervised machine learning. We are planning to add more models. Monitoring (Metrics)

To monitor this system in production we utilize grafana and prometheus to visualize the health of this machine learning system.

Feedback Loop (Fact-Store)

In addition we have a metadata registry for tracking feedback from false_positives in the machine learning system and to providing a method for ML to self correcting false predictions called the “fact-store”.