Nowadays, Artificial Intelligence (AI) and Machine Learning (ML) techniques are increasingly used to address complex classification problems, where defining an explicit algorithm capable of producing a reliable solution is not straightforward. Typical applications range from image and audio recognition to environmental monitoring and speech analysis. The performance of a model depends on both the chosen algorithm and parameter configuration, and is assessed through metrics that reveal its strengths and limitations. A key advantage is the separation between training and inference: while training is computationally intensive and can be done offline, inference is lightweight and suitable for IoT devices, enabling local data processing with lower latency and higher energy efficiency without relying on external servers.