As a microcontroller software developer, you likely start projects by identifying tools and software, setting up a development environment, and gathering evaluation boards and models.
Machine Learning (ML) applications follow the same pattern, but introduce additional complexity around the inclusion of machine learning models, software libraries for ML operations, and driver software to program neural processing unit (NPU) hardware.
The Corstone-300 and Corstone-310 reference designs are the basis of many ML IoT solutions. These designs offer a jump start on building hardware for ML applications. There are many software tools and examples available to get started creating ML applications, but you may find it difficult to see the big picture and understand which tools and software are best for you.
This Learning Path is to help you get started with Cortex-M and Ethos-U machine learning application development.