Book Image

Machine Learning with the Elastic Stack - Second Edition

By : Rich Collier, Camilla Montonen, Bahaaldine Azarmi
Book Image

Machine Learning with the Elastic Stack - Second Edition

By: Rich Collier, Camilla Montonen, Bahaaldine Azarmi

Overview of this book

Elastic Stack, previously known as the ELK stack, is a log analysis solution that helps users ingest, process, and analyze search data effectively. With the addition of machine learning, a key commercial feature, the Elastic Stack makes this process even more efficient. This updated second edition of Machine Learning with the Elastic Stack provides a comprehensive overview of Elastic Stack's machine learning features for both time series data analysis as well as for classification, regression, and outlier detection. The book starts by explaining machine learning concepts in an intuitive way. You'll then perform time series analysis on different types of data, such as log files, network flows, application metrics, and financial data. As you progress through the chapters, you'll deploy machine learning within Elastic Stack for logging, security, and metrics. Finally, you'll discover how data frame analysis opens up a whole new set of use cases that machine learning can help you with. By the end of this Elastic Stack book, you'll have hands-on machine learning and Elastic Stack experience, along with the knowledge you need to incorporate machine learning in your distributed search and data analysis platform.
Table of Contents (19 chapters)
Section 1 – Getting Started with Machine Learning with Elastic Stack
Section 2 – Time Series Analysis – Anomaly Detection and Forecasting
Section 3 – Data Frame Analysis

Chapter 1: 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 fully appreciate how Elastic 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 ML works. It also discusses the different kinds of analysis that can be done and the kinds of use cases that can be solved. Specifically, we will cover the following topics:

  • Overcoming the historical challenges in IT
  • Dealing with the plethora of data
  • The advent of automated anomaly detection
  • Unsupervised versus supervised ML
  • Using unsupervised ML for anomaly detection
  • Applying supervised ML to data frame analytics