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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
Author Profile Icon Bahaaldine Azarmi
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

Summary

Elastic ML's anomaly detection and forecasting analytics creates wonderful and meaningful results that are explorable via the rich UI that is provided in Kibana, or programmatically via direct querying of the results indices and the API. Understanding the results of your anomaly detection and forecasting jobs and being able to appropriately leverage that information for further custom visualizations or alerts makes those custom assets even more powerful.

In the next chapter, we'll leverage the results to create sophisticated and useful proactive alerts to further increase the operational value of Elastic ML.

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