Anomaly detection is the notion of automatically looking at data and finding outliers, i.e. data points that do not fit in well with the rest of the dataset. Finding an outlier is usually a sign that something exceptional has happened that should trigger further investigations or actions. Anomaly detection can be performed on existing historical datasets e.g. when you are pre-processing your AI training data. It can also be used on a real time data stream from your business transactions or sensor readings to almost instantly flag a data point as anomalous. This is a great example of AI automating things that a human can do and with a glance at a graph see a problem.
In this article Torbjörn will discuss anomaly detection from a few different perspectives and with this new tool under your belt you will start to look at the world with different eyes!
Follow the link below to read more about Torbjörn’s blog
Anomaly detection overview
