Please use this identifier to cite or link to this item:
http://hdl.handle.net/123456789/1998| Title: | Machine Learning with the Elastic Stack |
| Other Titles: | Expert techniques to integrate machine learning with distributed search and analytics |
| Authors: | Collier, Rich Azarmi, Bahaaldine |
| Keywords: | Machine Learning with the Elastic Stack |
| Issue Date: | Jan-2019 |
| Publisher: | Pact Publishing |
| Series/Report no.: | 300119; |
| Abstract: | Data analysis, manual charting, thresholding, and alerting have been an inherent part of IT and security operations for decades. Until the advent of sophisticated machine learning algorithms and techniques, much of the burden of proactive insight, problem detection, and root cause analysis fell onto the shoulders of the analysts. As the complexity and scale of modern applications and infrastructure has grown exponentially, it is apparent that humans need help. Elastic machine learning (ML) is an effective, easy-to-use solution for anomaly detection and forecasting use cases in relation to time-series machine data. This definitive elastic ML guide will get the reader proficient in the operation and techniques of advanced analytics without the need to be well-versed in data science. |
| URI: | http://hdl.handle.net/123456789/1998 |
| ISBN: | 78-1-78847-754-3 |
| Appears in Collections: | E-Books |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Machine Learning with the Elastic Stack -.pdf | 25.19 MB | Adobe PDF | View/Open |
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