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Serverful architectures, those that are built using resources administrated by the user, have been an effective way to accumulate resources for large scale computing for many years. Despite having some disadvantages, such as their low elasticity, up until now most of the efforts have been dedicated towards constructing libraries and frameworks designed to be executed on clusters of computers or virtual machines, which is the serverful architecture par excellence. Nonetheless, with the new advances in cloud computing coming from totally different approaches, serverful systems providing the best solution to large scale workflows started becoming, at least, an arguable assumption. In fact, the new wave of technologies could finally allow the implementation of transparency, a fairly promising aspect that grants developers the ability to design and program applications independently from the characteristics of resources that will be employed. Transparency, along with the elasticity presented by some of these new technologies, could potentially provide a better solution to the current approach to machine learning, nowadays the most popular branch from artificial intelligence due to its synergy with large datasets, since many of these algorithms are complex by design and they require more flexibility in the implementation.