Unsupervised NLP for Log Anomaly Detection
Channel: DevConf
Date: 2019-10-03

Speakers: Michael Clifford and Zak Hassan

As log data continues to grow, is it possible to automate analyzing logs to find anomalies and errors quickly with machine learning? Log anomaly detection is at its core an unsupervised natural language processing (NLP) Machine Learning problem that can be difficult to validate and tune in a production setting. However, if there was a system that could incorporate minimal user feedback during model training, we could offset some of these challenges.

In this talk, you will learn about the lessons learned from building log anomaly detection system in production giving specific emphasis to the technical challenges faced with, scalability, implementing a human-in-the-loop ML system and Integrating ETL with unsupervised ML to detect anomalies in application logs