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

Many high-velocity online business systems today have reached a point of such complexity that it is impossible for humans to pay attention to everything happening within the system. There are simply too many metrics and too many data points for the human brain to discern. 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. Thus, it has become imperative for companies to use machine learning in large-scale systems to analyze patterns of data streams and look for outliers. Consider an airline pricing system that calculates the price for each and every seat on all of its routes in order to maximize revenue. Seat pricing can change multiple times a day based on thousands of factors, both internal and external to the company. The airline must consider those factors when deciding to increase, decrease or hold a fare steady. An outlier in any given factor can be an opportunity to raise the price of a particular seat to increase revenue, or lower the price to ensure the seat gets sold.