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)

Building Elastic ML Canvas slides

In this section, we are going to build examples of a Canvas slide by showcasing real-time results from an Elastic ML job so that we can showcase the results in a very customized way.

Preparing your data

Before starting our workpad, we need to do some preparation so that we can use ML data in Canvas. We actually just need two things:

  • An Elastic ML job running and producing results
  • An index pattern pointing to the job results data

For the Elastic ML job, I'm going to use a single metric job that analyzes the traffic on a nginx web server by looking at the distinct count of IP interacting with the server.

The following Elastic ML analysis screenshot will give you an idea of the general...