Roy Ben-Alta, solution architect and principal business development manager at Amazon Web Services, and Anodot’s Chief Data Scientist Dr. Ira Cohen present various design patterns and share a solution implemented using Amazon Kinesis as a real-time event data processing pipeline that feeds Anodot’s AI-based analytics service, discovering and alerting on the anomalies in the data in real time and helping you avoid costly business incidents.

Get out of "Memento" mode and don’t be left guessing what’s happening with your business’ key data.

Yes, "Star Wars:, the classic space opera full of mystical intrigue and epic power struggles. Yet, at the center of the movie, data plays a critical role.

With millions of users across dozens of household-name and professional publications, PMC’s data science team needed a better way to track business incidents than Google Analytics’ alerting function. Read the case study to see how Anodot helped PMC identify positive business insights and use it to make editorial decisions.

Join our webinar from the Amazon Partner Series to hear how Lyft and other data-driven organizations benefit from uncovering hidden insights in real time with AI solutions from Amazon Web Services (AWS) and Anodot.

See how Foursquare used Anodot to shift from a manual, reactive process to automated, proactive application services management.

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

Major takeaways from my recent appearance at Amazon’s NYC AWS Loft with Amazon CTO Dr. Werner Vogels, where we discussed the present and future of machine learning and how it will push the boundaries of what's possible.

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.