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)

ML Tips and Tricks

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 the other chapters. It therefore made sense to give them a chapter all to themselves. Enjoy this potpourri of tips and tricks!

The following topics will be covered in this chapter:

  • Job groups
  • Ignoring time periods
  • Top-down alerting
  • Sizing ML deployments