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Machine Learning with the Elastic Stack - Second Edition

You're reading from  Machine Learning with the Elastic Stack - Second Edition

Product type Book
Published in May 2021
Publisher Packt
ISBN-13 9781801070034
Pages 450 pages
Edition 2nd Edition
Languages
Authors (3):
Rich Collier Rich Collier
Profile icon Rich Collier
Camilla Montonen Camilla Montonen
Profile icon Camilla Montonen
Bahaaldine Azarmi Bahaaldine Azarmi
Profile icon Bahaaldine Azarmi
View More author details

Table of Contents (19) Chapters

Preface 1. Section 1 – Getting Started with Machine Learning with Elastic Stack
2. Chapter 1: Machine Learning for IT 3. Chapter 2: Enabling and Operationalization 4. Section 2 – Time Series Analysis – Anomaly Detection and Forecasting
5. Chapter 3: Anomaly Detection 6. Chapter 4: Forecasting 7. Chapter 5: Interpreting Results 8. Chapter 6: Alerting on ML Analysis 9. Chapter 7: AIOps and Root Cause Analysis 10. Chapter 8: Anomaly Detection in Other Elastic Stack Apps 11. Section 3 – Data Frame Analysis
12. Chapter 9: Introducing Data Frame Analytics 13. Chapter 10: Outlier Detection 14. Chapter 11: Classification Analysis 15. Chapter 12: Regression 16. Chapter 13: Inference 17. Other Books You May Enjoy Appendix: Anomaly Detection Tips

Elastic ML job types

When we start using the Elastic ML UI to configure anomaly detection jobs, we will see that there are five different job wizards that are shown:

Figure 3.1 – The Create job UI showing different configuration wizards

The existence of these different configuration wizards implies that there are different "types" of jobs. In actuality, there is really only one job type—it is just that the anomaly detection job has many options, and many of these wizards make certain aspects of that configuration easier. Everything that you may desire to configure can be done via the Advanced wizard (or the API). In fact, when Elastic ML was first released as beta in v5.4, that was all that existed. Since then, the other wizards have been added for simplicity and usability in specific use cases.

An anomaly detection job has many configuration settings, but the two most important ones are the analysis configuration and the datafeed...

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