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Machine Learning with the Elastic Stack

You're reading from   Machine Learning with the Elastic Stack Gain valuable insights from your data with Elastic Stack's machine learning features

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Product type Paperback
Published in May 2021
Publisher Packt
ISBN-13 9781801070034
Length 450 pages
Edition 2nd Edition
Languages
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Authors (3):
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Camilla Montonen Camilla Montonen
Author Profile Icon Camilla Montonen
Camilla Montonen
Rich Collier Rich Collier
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Rich Collier
Bahaaldine Azarmi Bahaaldine Azarmi
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Bahaaldine Azarmi
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Toc

Table of Contents (19) Chapters Close

Preface 1. Section 1 – Getting Started with Machine Learning with Elastic Stack
2. Chapter 1: Machine Learning for IT FREE CHAPTER 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

Chapter 3: Anomaly Detection

Anomaly detection was the original capability of Elastic ML and is the most mature, stretching its roots back to the Prelert days (before the acquisition by Elastic in 2016). This technology is robust, easy to use, powerful, and broadly applicable to all kinds of use cases for time series data.

This jam-packed chapter will focus on using Elastic ML to detect anomalies in the occurrence rates of documents/events, rare occurrences of things, and numerical values outside of expected normal operation. We will run through some simple but effective examples that will highlight both the efficacy of Elastic ML and its ease of use.

Specifically, we will cover the following:

  • Elastic ML job types
  • Dissecting the detector
  • Detecting changes in event rates
  • Detecting changes in metric values
  • Understanding the advanced detector functions
  • Splitting analysis along categorical features
  • Understanding temporal versus population analysis
  • ...
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