Back in the days of the wild wild web (www) and post JQuery era, one web framework stood above all others: AngularJS. A “ring to rule them all”, AngularJS consolidated quite a few micro-frameworks and provided many extensibility points of expansion if needed. Over time though, many performance and architectural questions began to arise, to the point of no return – when the guys @Google decided to migrate from AngularJS to Angular (a poor naming decision).

Having just passed the 10-year anniversary of Malcolm Gladwell’s bestseller “Outliers: The Story of Success“, we thought to mark the occasion by taking a look at outliers and how they relate to success in the business world. Gladwell describes outliers as “those [people] who have been given opportunities — and who have had the strength and presence of mind to seize them.” At Anodot, we’ve also made it our mission to spot outliers, albeit of the data variety.

Simple enough to be embedded in text as a sparkline, but able to speak volumes about your business, time series data is the basic input of Anodot’s automated anomaly detection system. This article begins our three-part series in which we take a closer look at the specific techniques Anodot uses to extract insights from your data.

A little more than four years ago, Anodot started applying advanced AI/ML and unsupervised learning technologies to simplify monitoring challenges for DevOps teams. Today our company has customers from a variety of verticals and departments harnessing our unique platform to monitor business health, user behavior, product usage, IT ops, machine learning processes and even IoT.