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 the visualizations

Now that we have both raw data and some ML jobs completed on this data, let's begin the process of designing our own customized dashboard using a variety of standard Kibana visualization controls. But before we can do this, we need to let Kibana know that the ML results index exists, and that we want to plot data from this index.

Configuring the index pattern

To have Kibana recognize the data that's contained in the index that's storing the results of our ML jobs, we need to create an index pattern in the Management section of Kibana. Navigate to this part. Then, under the Kibana section, click on Index Patterns and then on Create index pattern.

In the Create index pattern UI, enable...