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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

Examining, exporting, and importing your trained models with the Trained Models API

You have prepared your dataset, trained your classification or regression model, looked at its performance, and determined that you would like to use it to enrich your production datasets. Before you can dive into ingest pipelines, inference processors, and the multitude of other components that you can configure to use your trained models, it is good to become familiar with the Trained Models API (https://www.elastic.co/guide/en/elasticsearch/reference/7.10/get-trained-models.html), a set of REST API endpoints that you can use to find out information about your models and even export them to other clusters. Let's take a tour of this API to see what it can tell us about our models.

A tour of the Trained Models API

In this section, we will take a practical look at using the Kibana Dev Console to examine things about our trained supervised models:

  1. Let's start in the Kibana Dev...
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