finelybook
Machine Learning with the Elastic Stack: Gain valuable insights from your data with Elastic Stack’s machine learning features,2nd Edition
by 作者: Rich Collier ,Camilla Montonen ,Bahaaldine Azarmi
Publisher Finelybook 出版社: Packt Publishing; 2nd edition (31 May 2021)
Language 语言: English
pages 页数: 450 pages
ISBN-10 书号: 1801070032
ISBN-13 书号: 9781801070034
Book Description
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
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 作者: 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 作者: 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
