A typical Internet of Things (IoT) system consists of a large number of different subsystems and devices, including sensors and actuators, gateways that connect them to the Internet, cloud services, end-user applications and analytics. Today, these subsystems are implemented with a broad variety of programming technologies and tools, making it difficult to migrate functionality from one subsystem to another. In our earlier papers, we have predicted the rise of \emph{isomorphic} IoT system architectures in which all the subsystems can be developed with a consistent set of technologies.
In this paper we expand the same research theme to machine learning technologies, highlighting the need to use ML in a consistent and uniform fashion across the entire Cloud-Edge continuum.