Total Cost of Ownsership for Smart Automation
August 26th, 2020
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Everything can be modelled as a time-series. Literally everything. And these time series can be implemented as event streams within software. With the right technology and the right mathematical models, great insights can be produced by analyzing events all around us. In this book go through the design and implementation of time-series forecasting system with Python, and we deploy it to Google Cloud. Another of our courses shows how to leverage this sytem to build a Failure Prevention System.
The standard way of training and deploying Machine Learning (ML) models is with centralized architectures. This means that there are centralized servers, that receive requests from devices and respond with ML predictions. These systems have the disadvantange, as with any other centralized system, that they can be overloaded and that they must have central access to all data. In this course we show how distributed ML systems can be trained and deployed in a way that eliminates some of the problems of centralized architectures.