Over the past ten years, Deep Learning has made great strides with significant advancements in a variety of Artificial Intelligence (AI) applications that range from image classification to speech recognition. Nevertheless, the unprecedented performance attained by Deep Neural Networks (DNNs) comes at the cost of high computational complexity and power consumption, making them unsuitable for deployment on resource-constrained devices such as embedded hardware. As a result, a field known as TinyML has emerged, aiming to develop efficient and accurate models for the ever-growing market of Internet-of-Things (IoT) devices. Moving both training and inference to the edge offers several advantages, including enhanced data privacy, lower latency, and improved energy efficiency.