Book Image

Machine Learning with the Elastic Stack

By : Rich Collier, Bahaaldine Azarmi
Book Image

Machine Learning with the Elastic Stack

By: Rich Collier, Bahaaldine Azarmi

Overview of this book

Machine Learning with the Elastic Stack is a comprehensive overview of the embedded commercial features of anomaly detection and forecasting. The book starts with installing and setting up Elastic Stack. You will perform time series analysis on varied kinds of data, such as log files, network flows, application metrics, and financial data. As you progress through the chapters, you will deploy machine learning within the Elastic Stack for logging, security, and metrics. In the concluding chapters, you will see how machine learning jobs can be automatically distributed and managed across the Elasticsearch cluster and made resilient to failure. By the end of this book, you will understand the performance aspects of incorporating machine learning within the Elastic ecosystem and create anomaly detection jobs and view results from Kibana directly.
Table of Contents (12 chapters)

Summary

While this chapter was not intended to be a full demonstration of the powerful features of Watcher, it is important to see that alerts can be created with ML's detailed results—both using built-in mechanisms and via custom definitions. And, if Elastic chooses to provide a different or updated Alerting platform in lieu of Watcher in the future, the fundamentals of what ML provides are unlikely to change much over time. The ultimate key take-away is that Elastic ML provides detailed results, stored in an Elasticsearch index, that can be queried and reported upon for the purposes of Alerting.

In the next chapter, Chapter 7, Using Elastic ML Data in Kibana Dashboards, we will also learn how to leverage ML's detailed results for custom visualizations and dashboards in Kibana.