Summary
We've seen that Elastic ML can highlight variations in volume, diversity, and uniqueness in metrics and log messages, including those that need some categorization first. Also, we've shown that population analysis can be an extremely interesting alternative to temporal anomaly detection when the focus is more on finding the most unusual entities. These techniques help solve the challenges we described before, where a human might struggle to recognize what is truly unusual and worthy of attention and investigation.
The skills learned in this chapter will be helpful in subsequent chapters, where we will see how ML assists in the process of getting to the root cause of complex IT problems, identifying application performance slowdowns, or when ML can assist in the identification of malware and/or malicious activity.
In the next chapter, we'll see how the expressive time series models built by anomaly detection jobs can be leveraged to forecast trends of your...
