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)

Holistic application visibility

IT operations and IT security organizations are collecting massive amounts of data. Some of that data is collected and/or stored in specialized tools, but some may be collected in general-purpose data platforms such as the Elastic Stack. But the question still remains: what percentage of that data is being paid attention to? By this, we mean the percentage of collected data that is actively inspected by humans, or being watched by some type of automated means (defined alarms based on rules, thresholds, and so on). Even generous estimates might put the percentage in the range of single digits. So, with 90% or more data being collected going unwatched, what's being missed? The proper answer might be that we don't actually know.

Before we admonish IT organizations for the sin of collecting piles of data, but not watching it, we need to understand...