Machine Learning in the Elastic Stack
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Machine learning for the Elastic Stack empowers you with simple tools to understand the behavior of your Elasticsearch data. What started with simple single- and multi-metrics anomaly detection jobs has grown into a powerful tool that automates notifications for anomalies and simplifies tasks like pre-configuring NGINX log analysis at scale. And there's more to show, including new features such as time series forecasting, which will allow you to predict system capacity and pre-empt issues, and automatic log data categorization.
Learn how to apply machine learning to your Elasticsearch data and see new features in action firsthand.
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.
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.