Here, ML has become a game changer for post-acquisition data analysis, such as image reconstruction , improvement of data by denoising and resolution enhancement and structure recognition . One of the bottle-necks for the efficiency with which electron microscopes can generate materials knowledge is also the investment of time and human, highly microscope-specific expertise required to align the instrument for optimal performance, especially, when the materials question to be solved requires switching between different modes of operation. While some big-data-trained ML models have already demonstrated to be capable of measuring aberrations very quickly , they are not yet capable of handling the complexity of a modern microscope which, for some instruments, requires managing more than 500 current supplies. A few groups are also applying ML methods towards real-time data analysis and automating experiments as illustrated in Fig. for the case of STEM . In contrast to the field of cryo-electron microscopy, where fully automated experiments can run for multiple days by repeating the same image acquisition process for automatically exchanged samples at many pre-defined sample positions, the complexity of adaptive ML-driven experiments in materials science (S)TEM experiments is much higher, given the inhomogeneity of most samples, the wide variety of signals to choose from and switch between, and the sequential process with which the data is acquired. Conventional ML methods used on already acquired data sets can simply not be applied one-to-one. New ML approaches for real-time applications in electron microscopy are still rare and most notably their onthe-fly implementation on the microscope has so far not been realized . The controlled components shown here are the scan coils and a programmable phase plate (inspired by the commercially available design by adaptem.eu), but it can also be lens currents, aberration-corrector settings, etc.