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.
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