Discover expert techniques for combining machine learning with the analytic capabilities of Elastic Stack and uncover actionable insights from your data

Key Features

• Integrate machine learning with distributed search and analytics
• Preprocess and analyze large volumes of search data effortlessly
• Operationalize machine learning in a scalable, production-worthy way

Book Description

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.

What you will learn

• 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

Who this book is for

If you're a data professional looking to gain insights into Elasticsearch data without having to rely on a machine learning specialist or custom development, then this Elastic Stack machine learning book is for you. You'll also find this book useful if you want to integrate machine learning with your observability, security, and analytics applications. Working knowledge of the Elastic Stack is needed to get the most out of this book.

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