Anodot: Ultimate Guide To Building A Machine Learning Outlier Detection System Part I


It has become a business imperative for high-velocity online businesses to analyze patterns of data streams and look for outliers that can reveal something unexpected. Most online companies already use data metrics to tell them how the business is doing, and detecting outliers in the data can lead to saving money or creating new business opportunities. This is where the online world is going; everything is data- and metricdriven to gauge the state of the business right now.

The types of metrics that companies capture and compare differ from industry to industry, and each company has its own key performance indicators (KPIs). What's more, regardless of industry, all online businesses measure the operational performance of their infrastructure and applications. Monitoring and analyzing these data patterns in real time can help detect subtle – and sometimes not-so-subtle – and unexpected changes whose root causes warrant investigation.