Nowadays artificial intelligence (AI) is one of hottest and the most debated topics in applied science. Being it applied to a plethora of different and possibly delicate use cases, both the technical performances and the understanding, explainability and trustworthiness of such technology are crucial in order for this to fit in human lives and become pervasive in the society. Despite the leap forward in performances occurred during the last few years, most of the machine Learning (ML) and deep learning (DL) algorithms still involve manual dataset-specific fine tuning, making the models' predictions tightly coupled with the input and the entire pipeline heavily problem-specific, hence requiring an high degree of domain expertise usually provided by ML experts.