1. A method for deriving diagnostic data about a technical system, comprising: utilizing learning metrics gained by at least one data driven learning process while generating and updating soft sensor models of said technical system by a process including: receiving sensor data sets from physical sensors which form part of subsystems within said technical system, the sensor data indicating an operation condition of at least one subsystem of the technical system, storing received sensor data sets in a database and splitting the received sensor data sets into a training data set and one or more test data sets, using the training data set to perform a multiple-instance training process to train a particular soft sensor model corresponding to a particular subsystem of the technical system, the multiple-instance training process including (a) an initial training process instance to build the particular soft sensor model, and (b) a sequence of follow-up training process instances to update the particular soft sensor model, wherein each training process instance for the particular soft sensor model generates training errors and test errors, storing the training errors and test errors generated by each instance of the iterative training process as learning metrics in the database, for each follow-up training process instance, performing an error comparison to detect a changing behaviour of the particular subsystem of the technical system corresponding with the particular soft sensor model, wherein the error comparison comprises at least one of: (a) comparing the training errors and test errors generated by that training process instance with each other, or (b) comparing at least one of the training errors or test errors generated by that training process instance with at least one of the training errors or test errors generated by a previously completed training process instance, and automatically generating a notification of the detected changing behaviour of the particular subsystem.