Now that there is an understanding of both the theoretical and practical operation of Elastic's ML, we can now focus our efforts on getting it properly installed and applying it to different use cases. The following chapters will lead us on the journey of solving some real-world problems in IT operations and IT security with Elastic's state-of-the-art automated anomaly detection.
Machine Learning with the Elastic Stack
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Machine Learning with the Elastic Stack
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Overview of this book
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.
By the end of this book, you will understand the performance aspects of incorporating machine learning within the Elastic ecosystem and create anomaly detection jobs and view results from Kibana directly.
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Current Title:
Machine Learning with the Elastic Stack
Table of Contents (12 chapters)
Preface
Free Chapter
Machine Learning for IT
Installing the Elastic Stack with Machine Learning
Event Change Detection
IT Operational Analytics and Root Cause Analysis
Security Analytics with Elastic Machine Learning
Alerting on ML Analysis
Using Elastic ML Data in Kibana Dashboards
Using Elastic ML with Kibana Canvas
Forecasting
ML Tips and Tricks
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Customer Reviews