In summary, big-data-based ML methods have already shown to be very powerful for post-processing tasks of electron microscopy data, but given the high complexity of these microscopes, their application in useful real-time data analysis and experiment automation methods still lags behind. Some initial developments of workflows that leverage ML methods to perform and optimize specific tasks of an electron microscope show promise for transitioning this fully human-controlled instrument to a (partially) autonomously operating machine being capable of carrying out precision measurements in a fully documented and fully reproducible manner. We expect that this development will largely increase the research output obtained from this type of instrumentation.