Videos & Podcasts | 1 min watch

Avoiding the App Trap: Using Anomaly Detection to Optimize Performance, Prevent Issues

by Anodot

Mobile app business models are often built around advertising and cross-promotions. Yet with so many moving parts — in the front-end and back-end for advertising platforms, partners and more —there are many opportunities for something to break. Like a butterfly wing creating a distant storm, if one element is working less than optimally, it can cause problems elsewhere. In this session at Strata Data San Jose 2017, Ira Cohen, Anodot’s Chief Data Scientist and co-founder, explains how anomaly detection can monitor all areas of your mobile app to fully optimize it.

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.

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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