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)

Machine Learning for IT

A decade ago, the idea of using machine learning (ML)-based technology in IT operations or IT security seemed a little like science fiction. Today, however, it is one of the most common buzzwords used by software vendors. Clearly, there has been a major shift in both the perception of the need for the technology and the capabilities that the state-of-the-art implementations of the technology can bring to bear. This evolution is important to understand to fully appreciate how Elastic's ML came to be and what problems it was designed to solve.

This chapter is dedicated to reviewing the history and concepts behind how Elastic's ML works. If you are uninterested and want to jump right into the installation and usage of the product, feel free to skip to Chapter 2, Installing the Elastic Stack with ML.