A decade ago, the idea of using machine learning (ML)-based technology in IT operations or IT security seemed a little like science fiction. Today, however, it is one of the most common buzzwords used by software vendors. Clearly, there has been a major shift in both the perception of the need for the technology and the capabilities that the state-of-the-art implementations of the technology can bring to bear. This evolution is important to fully appreciate how Elastic ML came to be and what problems it was designed to solve.
This chapter is dedicated to reviewing the history and concepts behind how Elastic ML works. It also discusses the different kinds of analysis that can be done and the kinds of use cases that can be solved. Specifically, we will cover the following topics:
- Overcoming the historical challenges in IT
- Dealing with the plethora of data
- The advent of automated anomaly detection
- Unsupervised versus supervised ML
- Using unsupervised ML for anomaly detection
- Applying supervised ML to data frame analytics