Nowadays, machine learning approaches have become an integral part in a wide variety of technologies and use-cases, ranging through a lot of diverse fields: from healthcare to automotive, or from banking to supply chain optimization, arriving also at the point of directly influencing our everyday’s life (e.g. with smart-home devices, or even just with smartphones). In each of the aforementioned, usually, a big quantity of data needs to be collected, so that the various algorithms which are applied can provide better models and thus, give results that most of the times coincide with the "ground truth". To improve these performances, what is usually done by several companies developing machine learning-based solutions, is to cyclically train from zero their models with bigger quantities of data, which are accumulated by their platforms over time.