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Anodot

Metrics At Scale: How to Scale and Manage Millions of Metrics (Part 2)

With businesses collecting millions of metrics, let’s look at how they can efficiently scale and deal with these amounts. As covered in the previous article (A Spike in Sales Is Not Always Good News), analyzing millions of metrics for changes may result in alert storms, notifying users about EVERY change, not just the most significant ones. To bring order to this situation, Anodot groups correlated anomalies together, in a unified alert.

3 Tips to Building Sustainable Product Quality (and Peace of Mind)

As product managers, you’re ultimately the one held responsible for the entire product. So the last thing you want to assume is that someone else has got monitoring and alerts covered. In the first days of a release, all eyes are on the new product or latest feature. Just a few months later, when you introduce a brand new feature, the old one might break in the process. At times like these, you want to be ahead of your users, and not hear from your users that something isn’t working.

The real-time journey from raw streaming data to AI-based analytics

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.

Using AI analytics to detect real-time application issues

Anodot’s Solutions Engineer and AI specialist, Steven Kirkpatrick, presents how leading gaming companies are leveraging the powerful anomaly detection capabilities of Anodot’s AI Analytics to proactively address business incidents in real time, minimizing revenue loss and identifying opportunities.