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)

Data organization

Before we can effectively wrangle all of this underlying data, we need to smartly segment it, possibly enrich it, and leverage the contextual information contained within it. First, we will focus on segmentation and enrichment.

Effective data segmentation

Simply by virtue of collecting some types of data (system performance metrics, log files, and so on) from underlying servers/hosts, there is likely already a natural segmentation of the data by server/host. Let's look at a sample measurement from Metricbeat:

{  
"_index":"metricbeat-6.0.0-2018.01.01",
"_type":"doc",
"_id":"ZQtas2ABB_sNnq-vMrgR",
"_score":1,
"_source...