Book Image

Machine Learning with the Elastic Stack

By : Rich Collier, Bahaaldine Azarmi
Book Image

Machine Learning with the Elastic Stack

By: Rich Collier, Bahaaldine Azarmi

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.
Table of Contents (12 chapters)

Forecasting versus prophesying

Past performance is not indicative of future results. This disclaimer is used by financial companies when they reference the performance of products such as mutual funds. But this disclaimer is a bit of an odd contradiction, because the past is all that we have to work with. If the companies that comprise the mutual fund have consistently had positive quarterly results for the last eight quarters straight, does that guarantee that they will also have a positive set of results for the next eight quarters and that their public valuation will continue to rise? Probability could be on the side of that being the case, but that might not be the whole story. And, before we get too wishful in thinking that Elastic ML's ability to forecast is our key to making a fortune in the stock market, we should be realistic about one key caveat.

The reason financial...