An approach to intelligent additive manufacturing makes use of one or more of machine learning, feedback using machine vision, and determination of machine state. In some examples, a machine learning transformation receives data representing a partially fabricated object and a model of an additional part (e.g., layer) of the part, and produces a modified model that is provided to a printer. The machine learning predistorter can compensate for imperfections in the partially fabricated object as well as non-ideal characteristics of the printer, thereby achieving high accuracy.