Time series and event data form the basis for real-time insights about the performance of businesses such as eCommerce, IoT, and web services, but gaining these insights involves designing a learning system that scales to millions and billions of data streams.
In this session from the 2016 Strata Data Conference in New York, Anodot Co-Founder and Chief Data Scientist Ira Cohen outlines a system that performs real-time machine learning and analytics on streams at massive scale.
Presented by Dr. Ira Cohen
Dr. Ira Cohen co-founded Anodot and serves as the company’s chief data scientist, where he develops and invents real-time multivariate anomaly detection algorithms capable of analyzing millions of time series signals. He holds a Ph.D in machine learning from the University of Illinois at Urbana-Champaign and has more than 12 years of industry experience.
