The process is very easy as we use the Custom Vision (
) service to train an image classifier and then export the finished model in a number of different forms – CoreML, TensorFlow, ONNX, or dockerfile for IoT Edge/Azure Functions/Azure ML.
So the process is:
Use CustomVision.ai to build a model
Export the modal appropriately e.g. TensorFlow
Use boiler plate IoT Edge module code and swap in new modal files.
Custom test data source as appropriate and execute against models
The evolution of this is to exploit the Myriad processors from Intel by running a model – such as those provided by Intel’s OpenVino – on the Myriad processor under the control of a Pi. This has enabled some significant CNN models to be executed.
The Embedded Learning Library (ELL) allows you to design and deploy intelligent machine-learned models onto resource constrained platforms and small single-board computers, like Raspberry Pi, Arduino, and micro:bit. The deployed models run locally, without requiring a network connection and without relying on servers in the cloud. ELL is an early preview of the embedded AI and machine learning technologies developed at Microsoft Research.
Go to our
for tutorials, instructions, and a gallery of pretrained ELL models for use in your projects.