What Is Embedded Machine Learning?

Embedded machine learning involves deploying machine learning algorithms on microcontrollers, allowing them to run on small devices like Raspberry Pi, NVIDIA Jetson, Intel Movidius, and Luxonis OAK, rather than relying on powerful supercomputers or cloud-based systems. This approach benefits from advancements in smaller, more efficient models and increasingly affordable, powerful hardware. Embedded machine learning provides several advantages, including faster processing speeds without server round-trips, offline functionality, and enhanced privacy due to local data processing. However, it also faces constraints such as the need for smaller models that might reduce accuracy and challenges in implementing active learning for model improvement. In the context of computer vision, deploying models to edge devices requires setting up the necessary dependencies, often utilizing tools like Docker for managing these environments. Despite the complexity, this method offers unique benefits, such as increased inference speeds and local data processing.