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)

Introduction to Canvas

This section presents the key aspects of Canvas by walking through some live examples using Canvas itself. Canvas is part of the default distribution of the Elastic Stack as of version 6.5.

What is Canvas?

As explained previously, Canvas is a place for you to build highly custom-tailored reports with a set a customizable elements. The experience in Canvas is very different from standard Kibana dashboards. Canvas presents you with a workspace where you can build sets of slides (similar in concept to Microsoft PowerPoint) called the workpad.

Here is a screenshot of an empty Canvas Workpad:


As we can see, the workspace has a blank page where you can place and position components called elements. Elements...