Introducing Machine Learning for the Elastic Stack

Version 5.4 introduces machine learning features (beta) to the Elastic Stack. These new features, available via X-Pack, let you automatically model the normal behavior of your Elasticsearch time series data in real time to identify anomalies, streamline root cause analysis, and reduce false positives.

In this short video, Sophie Chang and Steve Dodson, machine learning leads at Elastic, explain how machine learning can help organizations overcome the challenges with current anomaly detection techniques.

Steve Dodson

Dr. Steve Dodson is Tech Lead, Machine Learning at Elastic. He was previously founder and CTO at Prelert (acquired by Elastic in Sept 2016). Steve has over 18 years of experience in enterprise systems and software development, focused on large distributed systems, complex event processing and machine learning. Prior to software development, Steve worked in the Computational Mechanics group at Imperial College, London where he delivered key contributions to the field, resolving scalability issues using a novel approach to solving Maxwell's equations which allowed it to become a practical technique used today by major companies.

Sophie Chang

Sophie Chang is Team Lead for Machine Learning at Elastic. Formerly VP Engineering at Prelert, she is an experienced engineering manager, with a background in enterprise software and systems management. She has a degree in Physics from Imperial College.