A User-Driven Knowledge Center
June 29th, 2020
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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.
DELTA LAB has a user-driven, evergrowing, knowledge center for Smart Automation. It's user-driven because you can help prioritize content by leaving comments on placeholders that interest you. It's evergrowing not only because new content is consistently published, but also because it is continuously updated. In this post we go through how it works, what it will provide you, and how you can make the most out of it.