Our customer success team offers a brief introduction to machine learning anomaly detection for monitoring your partner networks and affiliate programs. In addition to actual customer alerts, we show the ROI they've seen with early incident detection.

Gaming giant King, the creators of "Candy Crush" and "Farm Heroes", share ways in which Anodot's AI analytics is helping them catch service disruptions and outages much earlier. In addition to discussing how to shorten TTD, this panel of experts shares a tool for estimating the cost of downtime at your company.

Learn what it takes to build time series anomaly detection, from industry experts Arun Kejariwal, Ira Cohen and Ben Lorica, who share valuable advice for ways to successfully implement and execute anomaly detection systems in today’s increasingly complex corporate world.

Ready to move into the future of BI and AI-driven business monitoring? Listen to: Greg Kurzhals, Product Analyst at Pandora, Matt Aslett, Research Director for the Data Platforms and Analytics Channel at 451 Research, and Dr. Ira Cohen, Chief Data Scientist at Anodot

"Anodot shows us an anomaly in real time, and correlates it to a specific event, like a feature update. With Anodot, we know about issues immediately.”

In this session at White Hall's BDA conference, AppNexus VP of Engineering Travis Johnson discusses how the company uses Anodot’s AI Analytics for insights about unknown unknowns, to confidently and proactively address incidents quickly.

In this session at O’Reilly's Artificial Intelligence Conference, Dr. Ira Cohen, Anodot's Chief Data Scientist and co-founder, presents how Anodot devised a way to intelligently monitor the performance of highly complex, unsupervised machine learning models with a “learning the learner” approach.

Credit Karma shares the benefits of using Anodot to easily track data, get real-time alerts and altogether promote a fair and healthy business monitoring.

In this session at the Strata Data Conference, Anodot’s Inbal Tadeski presents the importance of identifying relationships between time metrics so that they can be used for predictions, root cause diagnosis, and more.