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)

Using Elastic ML Data in Kibana Dashboards

At this point of the book, you have seen multiple use cases and multiple ways to leverage the output of Elastic ML, such as getting proactive alerts. Whether you are in a DevOps team or a security team, you will likely need to visualize your data. Visualizing lots of raw data can be a burden, as too many data sources overwhelm what our eyes can effectively capture. This is where the visualization capabilities of Kibana, combined with Elastic ML data, can be leveraged to highlight what really matters in dashboards.

This chapter will walk you through the creation of visualizations and dashboards that use Elastic ML data. In this manner, analysts who are not familiar with ML, but need to visualize the data, can be helped throughout their investigation.

First, we'll have a quick tour through Kibana and list the visualization options...