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.
If you are an IT professional eager to gain further insights into machine data within Elasticsearch without having to rely on an ML specialist or custom development, ML with the Elastic Stack is for you. Those looking to augment manual data analysis with automated, advanced anomaly detection and forecasting will find this book very useful. Prior experience with the Elastic Stack will be helpful in order to get the most out of this book.