I really wouldn’t want to pour cold water on this idea, but from what (little) I know, ML works best with very large datasets, like millions of data points for training. Our dataset is by no means small, but the data on kettles, for example, is unlikely to relate well to that on smartphones, and hence the dataset on each individual class of item is not that big. Some of the successful applications I’m aware of are in machine translation and speech recognition (Google has maybe petabytes of textual data in different languages to train on), medical diagnosis (virtually an entire national population’s data might be available) and malware analysis (again, huge datasets of both innocuous and malicious samples are availble).
As for live prediction of fixing clues then it might even be unhelpful. If the fault is at all obscure then what you need most is an open mind and good deductive powers. Not to say that hints or previous experirence aren’t useful, but an inscrutable ML could lead to tunnel vision.
Insights into the changing profile of failures may be more promising, but this needs skillful application of statistical techniques rather than ML.
For all that, great that we’ve got the free tickets. @Monique and @Elena will certainly come back with a much better appreciation of where ML can be usefully applied, and if any of that is actionable I guess it might be in an area none of us will have thought of.