Machine Learning with the Elastic Stack: Gain valuable insights from your data with Elastic Stack's machine learning features

by Rich Collier, Camilla Montonen, Bahaaldine Azarmi

Overview

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.
By the end of this Elastic Stack book, you'll have hands-on machine learning and Elastic Stack experience, along with the knowledge you need to incorporate machine learning in your distributed search and data analysis platform.

Product Details

ISBN-13:	9781801078467
Publisher:	Packt Publishing
Publication date:	05/31/2021
Sold by:	Barnes & Noble
Format:	eBook
Pages:	450
File size:	31 MB
Note:	This product may take a few minutes to download.

About the Author

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, or Baha for short, is the head of solutions architecture in the EMEA South region at Elastic. Prior to this position, Baha co-founded ReachFive, a marketing data platform focused on user behavior and social analytics. He has also worked for a number of different software vendors, including Talend and Oracle, where he held positions as a solutions architect and architect. Prior to Machine Learning with the Elastic Stack, Baha authored books including Learning Kibana 5.0, Scalable Big Data Architecture, and Talend for Big Data. He is based in Paris and holds an MSc in computer science from Polytech'Paris.

Table of Contents

Table of Contents

1. Machine Learning for IT
2. Enabling and Operationalization
3. Anomaly Detection
4. Forecasting
5. Interpreting Results
6. Alerting on ML Analysis
7. AIOps and Root Cause Analysis
8. Anomaly Detection in Other Elastic Stack Apps
9. Introducing Data Frame Analysis
10. Outlier Detection
11. Classification Analysis
12. Regression
13. Inference
14. Appendix: Anomaly Detection Tips