Book Image

Machine Learning with the Elastic Stack

By : Rich Collier, Bahaaldine Azarmi
Book Image

Machine Learning with the Elastic Stack

By: Rich Collier, Bahaaldine Azarmi

Overview of this book

Machine Learning with the Elastic Stack is a comprehensive overview of the embedded commercial features of anomaly detection and forecasting. The book starts with installing and setting up Elastic Stack. You will perform time series analysis on varied kinds of data, such as log files, network flows, application metrics, and financial data. As you progress through the chapters, you will deploy machine learning within the Elastic Stack for logging, security, and metrics. In the concluding chapters, you will see how machine learning jobs can be automatically distributed and managed across the Elasticsearch cluster and made resilient to failure. By the end of this book, you will understand the performance aspects of incorporating machine learning within the Elastic ecosystem and create anomaly detection jobs and view results from Kibana directly.
Table of Contents (12 chapters)

Installing the Elastic Stack with Machine Learning

In Chapter 1, Machine Learning for IT, we learned what anomaly detection for IT is by looking at the fundamental steps behind the theory. Most importantly, we learned that, thanks to the Elastic Stack, Elastic machine learning (ML) allows us to operationalize anomaly detection, from analysis to visualization. Now, in this chapter, we'll roll up our sleeves and get to work installing the entire Elastic Stack. By doing so, we'll have a better understanding of the anatomy of Elastic ML. In this chapter, we will be covering the following topics:

  • Installing Elasticsearch
  • Installing Kibana
  • Enabling ML features
  • Elastic ML features