This is the code repository for Machine Learning with the Elastic Stack - Second Edition, published by Packt.
Gain valuable insights from your data with Elastic Stack's machine learning features
Elastic Stack, previously known as the ELK stack, is a log analysis solution that helps users ingest, process, and analyze search data effectively. With the addition of machine learning, a key commercial feature, the Elastic Stack makes this process even more efficient. This updated second edition of Machine Learning with the Elastic Stack provides a comprehensive overview of Elastic Stack's machine learning features for both time series data analysis as well as for classification, regression, and outlier detection. The book starts by explaining machine learning concepts in an intuitive way. You'll then perform time series analysis on different types of data, such as log files, network flows, application metrics, and financial data. As you progress through the chapters, you'll deploy machine learning within Elastic Stack for logging, security, and metrics. Finally, you'll discover how data frame analysis opens up a whole new set of use cases that machine learning can help you with.
This book covers the following exciting features:
- Find out how to enable the ML commercial feature in the Elastic Stack
- Understand how Elastic machine learning is used to detect different types of anomalies and make predictions
- Apply effective anomaly detection to IT operations, security analytics, and other use cases
- Utilize the results of Elastic ML in custom views, dashboards, and proactive alerting
- Train and deploy supervised machine learning models for real-time inference
- Discover various tips and tricks to get the most out of Elastic machine learning
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:
18/05/2020 15:16:00 DB Not Updated [Master] Table
Following is what you need for this book: You will need a system with a good internet connection and an Elastic account
With the following software and hardware list you can run all code files present in the book (Chapter 1-15).
| Chapter | Software required | OS required |
|---|---|---|
| 1-12 | Elastic Cloud | Windows, Mac OS X, and Linux (Any) |
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.
Camilla Montonen is a Senior Machine Learning Engineer at Elastic.
Bahaaldine Azarmi 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.