Inverse sets [1] defined in 2011 and, independently, in [2] (2012) provide a concise mathematical description of indexing methods that deal efficiently with vast amounts of data. On the other hand, traditional, non-indexing machine learning methods are far more computationally demanding. For instance, aircraft engine prediction maintenance needs analyzing statistical records of hundreds of operational cycles of thousands of engines - a processing, which takes Microsoft’s cloud technologies and unspecified machine learning time. However, it was found that inverse sets-based approaches require only a PC or a smartphone platform providing practically instantaneous learning and prediction time.