A device twin is a set of tools designed to help us work with a fleet. They can be used to pass information down to a device, such as what model that device should be using. They can be used to pass more stateful information back to the cloud, such as the model's actual error rate.
Device twins have two sides. On the device side, there is a JSON file that acts like a writable configuration file, while on the cloud side, there is a writable database of properties. These two sides sync in an orderly way to allow you to reason about your fleet.
One advantage of a device twin is that you can see if model deployment actually worked. Often, machine learning models are updated with information changes, and new models are pushed down to the devices. These models can trigger out-of-memory exceptions and fail; they can also brick the device. Often, in an IoT product's life cycle, hardware may be substituted if a manufacture changes or certain components...
