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)

Bringing it all together for root cause analysis

We are at the point now where we can now discuss how we can bring everything together. In our desire to increase our effectiveness in IT operations and look more holistically at application health, we now need to operationalize what we've prepared in the prior sections and configure our ML jobs accordingly. To that end, let's work through a real-life scenario in which ML helped us get to the root cause of an operational problem.

Outage background

This scenario is loosely based on a real application outage, although the data was somewhat simplified and sanitized to obfuscate the original user. The problem was with a retail application that processed gift card transactions...