Improving Performance Engineering with Machine Learning

Had never heard this specifically positioned this way, nice idea.   Needs some more detail to explain, how it has been done, but a great start.   We did a form of this with business process modelling, and the integration with machine learning to determine how elements of the process performed.   Not sure if this is quite the same thing.  Like the anomaly reference.

Machine Learning: How it Improves Performance Engineering in DSC    Posted by Ryan Williamson  

Enterprise software, as well as other kinds, remains a complicated endeavor, thus necessitating the use of modern means to gauge, analyze, and adapt their performance. And one of the most popular technologies in the performance engineering market right now is machine learning. Since it has demonstrated an unparalleled ability to not only help foresee performance issues and fix them. When used in the right manner — this combination can also help performance engineering teams to steer clear of any issues at all completely. It is because machine learning comes equipped with the ability to interpret and analyze data in real-time, thus delivering valuable insights about the system’s performance.

However, if you are going to truly leverage machine learning’s abilities in the context of performance engineering, it is first essential to understand the basics. Through this article, let’s discuss the kind of performance anomalies one can encounter.

Point anomalies: This is when there is only one data point that is distinct from the entire set.
Contextual anomalies: In this scenario, the anomaly is contextual, i.e., exists only in a particular context.
Collective anomalies: This refers to a data set that exhibits signs of an anomaly.  ....  "