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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

Understanding the importance and limitations of KPIs

Because of the problem of scale and the desire to make some amount of progress in making the collected data actionable, it is natural that some of the first metrics to be tackled for active inspection are those that are the best indicators of performance or operation. The KPIs that an IT organization chooses for measurement, tracking, and flagging can span diverse indicators, including the following:

  • Customer experience: These metrics measure customer experience, such as application response times or error rates.
  • Availability: Metrics such as uptime or Mean Time to Repair (MTTR) are often important to track.
  • Business: Here we may have metrics that directly measure business performance, such as orders per minute or number of active users.

As such, these types of metrics are usually displayed, front and center, on most high-level operational dashboards or on staff reports for employees ranging from technicians...

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