Summary

To summarize what we discussed in this chapter, we covered the genesis story of ML in IT—born out of the necessity to automate analysis of the massive, ever-expanding growth of collected data within enterprise environments. We also got a more intuitive understanding of the different types of ML in Elastic ML, which includes both unsupervised anomaly detection and supervised data frame analysis.

As we journey through the rest of the chapters, we will often be mapping the use cases of the problems we're trying to solve to the different modes of operation of Elastic ML.

Remember that if the data is a time series, meaning that it comes into existence routinely over time (metric/performance data, log files, transactions, and so on), it is quite possible that Elastic ML's anomaly detection is all you'll ever need. As you'll see, it is incredibly flexible and easy to use and accomplishes many use cases on a broad variety of data. It's kind of a Swiss Army knife! A large amount of this book (Chapters 3 through 8) will be devoted to how to leverage anomaly detection (and the ancillary capability of forecasting) to get the most out of your time series data that is in the Elastic Stack.

If you are more interested in finding unusual entities within a population/cohort (User/Entity Behavior), you might have a tricky decision between using population analysis in anomaly detection versus outlier detection in data frame analytics. The primary factor may be whether or not you need to do this in near real time—in which case you might likely choose population analysis. If near real time is not necessary and/or if you require the consideration of multiple features simultaneously, you would choose outlier detection. See Chapter 10, for more detailed information about the comparison and benefits of each approach.

That leaves many other use cases that require a multivariate approach to modeling. This would not only align with the previous example of real estate pricing but also encompass the use cases of language detection, customer churn analysis, malware detection, and so on. These will fall squarely in the realm of the supervised ML of data frame analytics and be covered in Chapters 11 through 13.

In the next chapter, we will get down and dirty with understanding how to enable Elastic ML and how it works in an operational sense. Buckle up and enjoy the ride!