Web Access Logs in Elasticsearch and Machine Learning
Elasticsearch and the machine learning features of X-Pack helps users quickly identify anomalies in their data to avoid costly outages or data loss. This presentation will give a live demo of how web access logs can be analyzed to help identify early signals for cascading failures and unusual users.
Join Steve Dodson, technical lead machine learning and Tom Grabowski, principal product manager machine learning, for a live demo covering:
- Ingesting NGINX logs with filebeat
- Analyzing these logs in machine learning
- Data visualization in Kibana
- Forecasting to predict resource requirements with machine learning
You'll also receive an email with related content
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.
Thomas Grabowski is a member of the Product Management team and focuses on X-pack, Machine Learning. Prior to joining Elastic, he has spent the last two decades in IT Log Operations and Analytics market. Thomas co-founded two companies, LogLogic and RapidEngines, which were both acquired.