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

Elastic ML has an additional feature over and above anomaly detection, the ability to take and extrapolate the time series models into the future for forecasting purposes. With use cases that include advanced breach detection and capacity planning, this feature alleviates the human burden of manually charting, tracking, and predicting where things are going in the future, based upon how they have behaved in the past.

In our next and final chapter, Chapter 10, ML Tips and Tricks, we'll run through a plethora of practical tips and tricks that didn't find a home in the other chapters.