Anodot Chief Data Scientist Ira Cohen and Arun Kejariwal, who led an in-house anomaly detection system at Twitter, share a novel two-step approach to building more reliable prediction models with the integration of anomalies.

Their talk, presented at O’Reilly Artificial Intelligence Conference 2019, outlines the benefits of coupling correlation analysis – using deep learning techniques – with anomaly detection and details the potential challenges based on production data.



Ira Cohen is co-founder and chief data scientist at Anodot, where he’s responsible for developing and inventing the company’s real-time multivariate anomaly detection algorithms that work with millions of time series signals. He holds a PhD in machine learning from the University of Illinois at Urbana-Champaign and has more than 12 years of industry experience.

Arun Kejariwal is an R&D leader and data enthusiast who has previously served in management roles at Twitter, Netflix, Yahoo! and Machine Zone. He has also authored numerous publications statistical and machine learning, time series analysis, data-driven mobile marketing, software development, and hardware design. He is a self-described advocate of open source.

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Written by Anodot

Anodot leads in Autonomous Business Monitoring, offering real-time incident detection and innovative cloud cost management solutions with a primary focus on partnerships and MSP collaboration. Our machine learning platform not only identifies business incidents promptly but also optimizes cloud resources, reducing waste. By reducing alert noise by up to 95 percent and slashing time to detection by as much as 80 percent, Anodot has helped customers recover millions in time and revenue.

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