Book Image

Machine Learning with the Elastic Stack - Second Edition

By : Rich Collier, Camilla Montonen, Bahaaldine Azarmi
5 (1)
Book Image

Machine Learning with the Elastic Stack - Second Edition

5 (1)
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

Appendix: Anomaly Detection Tips

As we wind down the content for this book, it occurred to us that there's still a plethora of good, bite-sized explanations, examples, and pieces of advice that didn't quite fit into sections of the other chapters. It therefore made sense to give them a home all to themselves here in the Appendix. Enjoy this potpourri of tips, tricks, and advice!

The following topics will be covered here in the Appendix:

  • Understanding influencers in split versus non-split jobs
  • Using one-sided functions to your advantage
  • Ignoring time periods
  • Using custom rules and filters to your advantage
  • Anomaly detection job throughput considerations
  • Avoiding the over-engineering of a use case
  • Using anomaly detection on runtime fields