The good news is that Tiny Machine Learning (TinyML) sidesteps all those downsides while tacking on data privacy as the cherry on top.
TinyML is a technique or field of study in machine learning and embedded systems that explores which machine-learning applications (once reduced, optimized and integrated) can be run on devices as small as microcontrollers.
Pete Warden (TensorFlow Lite Micro), Kwabena Agyeman (Arm Innovator), and Daniel Situnayake (Edge Impulse) are recognized as early influencers and “founding fathers” of TinyML because machine learning is so widely used on the Internet-of-things (IoT) and in small, portable devices, it’s no wonder the development of TinyML happened as quickly as it did and garnered so much attention and early adoption.
In 2030, ABI Research predicts the shipment of approximately 2.5 billion devices that feature TinyML.
And, in just the next five years, Silent Intelligence forecasts that TinyML could “reach more than $70 billion in economic value.”
Uses for TinyML are far-ranging and address convenience, communication, knowledge-sharing, matchmaking, entertainment, agriculture, predictive maintenance, and healthcare, to name a few.
Today, the most common fields for TinyML application include audio analytics, pattern recognition, and voice human-machine interfaces.”
Here are just a few of the many compelling ways TinyML can improve processes, reduce costs, and increase the quality of life.