This is the code repository for Machine Learning with the Elastic Stack, published by Packt.
Expert techniques to integrate machine learning with distributed search and analytics
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.
This book covers the following exciting features:
- Install the Elastic Stack to use machine learning features
- Understand how Elastic machine learning is used to detect a variety of anomaly types
- Apply effective anomaly detection to IT operations and security analytics
- Leverage the output of Elastic machine learning in custom views, dashboards, and proactive alerting
- Combine your created jobs to correlate anomalies of different layers of infrastructure
If you feel this book is for you, get your copy today!
All of the code is organized into folders. For example, Chapter02.
The code will look like the following:
"actions": {
"log": {
"logging": {
"level": "info",
"text": "Alert for job
Following is what you need for this book: If you are a data professional eager to gain insight on Elasticsearch data without having to rely on a machine learning specialist or custom development, Machine Learning with the Elastic Stack is for you. Those looking to integrate machine learning within their search and analytics applications will also find this book very useful. Prior experience with the Elastic Stack is needed to get the most out of this book.
With the following software and hardware list you can run all code files present in the book (Chapter 1-10).
| Chapter | Software required | OS required |
|---|---|---|
| 2-10 | Elasticsearch, kibana, | Windows, Mac OS X, and Linux (Any) |
| metricbeat, packetbeat, |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
Rich Collier is a solutions architect at Elastic. Joining the Elastic team from the Prelert acquisition, Rich has over 20 years' experience as a solutions architect and pre-sales systems engineer for software, hardware, and service-based solutions. Rich's technical specialties include big data analytics, machine learning, anomaly detection, threat detection, security operations, application performance management, web applications, and contact center technologies. Rich is based in Boston, Massachusetts
Bahaaldine Azarmi or Baha for short, is a solutions architect at Elastic. Prior to this position, Baha co-founded ReachFive, a marketing data platform focused on user behavior and social analytics. Baha also worked for different software vendors such as Talend and Oracle, where he held solutions architect and architect positions. Before Machine Learning with the Elastic Stack, Baha authored books including Learning Kibana 5.0, Scalable Big Data Architecture, and Talend for Big Data. Baha is based in Paris and has an MSc in computer science from Polytech'Paris.
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