Chapter 5: Running Machine Learning Jobs on Elasticsearch
In the previous chapter, we looked at how large volumes of data can be managed and leveraged for analytical insight. We looked at how changes in data can be detected and responded to using rules (also called alerts). This chapter explores the use of machine learning techniques to look for unknowns in data and understand trends that cannot be captured using a rule-based approach.
Machine learning is a dense subject with a wide range of theoretical and practical concepts to cover. In this chapter, we will focus on some of the more important aspects of running machine learning jobs on Elasticsearch. Specifically, we will cover the following:
- Preparing data for machine learning
- Running single- and multi-metric anomaly detection jobs on time series data
- Classifying data using supervised machine learning models
- Running machine learning inference on incoming data
