Machine learning architectures

Until now, we have discussed single methods that could be employed to solve specific problems. However, in real contexts, it's very unlikely to have well-defined datasets that can be immediately fed into a standard classifier or clustering algorithm. A machine learning engineer often has to design a full architecture that a layman would consider to be like a black box, where the raw data enters and the outcomes are automatically produced. All the steps necessary to achieve the final goal must be correctly organized and seamlessly joined together in a processing chain similar to a computational graph (indeed, it's very often a direct acyclic graph). Unfortunately, this is an unconventional process, as every real-life problem has its own peculiarities. However, there are some common steps that are normally included in almost any ML pipeline...