We've seen that ML can highlight variations in volume, diversity, and uniqueness in log lines, including those that need some categorization first. These techniques help solve the challenges we described in the first part of this chapter, where a human must both recognize the uniqueness of the content and the relative frequency of occurrence of each raw log message.
The skills learned in this chapter will be helpful in the next chapter, Chapter 4, IT Operational Analytics and Root Cause Analysis, where we will use ML to assist in the process of getting to the root cause of a complex problem that spans multiple datasets, including log files and performance metrics. The analysis will most certainly include the detection of unusually occurring log events.