Jose Vicente Sáez

TinyML has proven to be a powerful tool for implementing machine learning models in devices and environments with limited resources. In this article, we explore its potential in the refinery and chemical sector.

We have seen especially during the last few months how model releases with billions of parameters requiring high processing power have been reproduced. On the other hand, there is also a growing trend that revolves around the ability to run lightweight models in real-time without the need for constant connection on low-power devices such as microcontrollers, sensors, and other embedded systems which is also revolutionizing the AI industry. This trend is known as TinyML.