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Mastering Kibana 6.x

You're reading from   Mastering Kibana 6.x Visualize your Elastic Stack data with histograms, maps, charts, and graphs

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Product type Paperback
Published in Jul 2018
Publisher Packt
ISBN-13 9781788831031
Length 376 pages
Edition 1st Edition
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Author (1):
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Anurag Srivastava Anurag Srivastava
Author Profile Icon Anurag Srivastava
Anurag Srivastava
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Table of Contents (16) Chapters Close

Preface 1. Revising the ELK Stack FREE CHAPTER 2. Setting Up and Customizing the Kibana Dashboard 3. Exploring Your Data 4. Visualizing the Data 5. Dashboarding to Showcase Key Performance Indicators 6. Handling Time Series Data with Timelion 7. Interact with Your Data Using Dev Tools 8. Tweaking Your Configuration with Kibana Management 9. Understanding X-Pack Features 10. Machine Learning with Kibana 11. Create Super Cool Dashboard from a Web Application 12. Different Use Cases of Kibana 13. Creating Monitoring Dashboards Using Beats 14. Best Practices 15. Other Books You May Enjoy

Machine learning jobs


In Kibana in order to run any machine learning analysis, we used to create the machine learning job and execute that in order to get the result. A job contains the configuration and meta-data information needed to perform the machine learning task. There are one or more detectors for each job that applies the analytical function to specific fields of our data. The job also has properties that affect the anomaly consideration through types of events or entities. For example, we can specify that entities are analyzed based on other entities in data, or relative to their own previous behavior.

For running machine learning jobs, we have a UI in Kibana through which we can execute different types of machine learning jobs. We need to click on the machine learning link on the left-hand, which will open the following screen:

In this screenshot, we can see an empty page with a button because we have not created any job yet. By clicking on Create new job, we can open the index...

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