In addition to the requirement for very fast data processing and fast access to electron optical components of the microscope, method developments will also need to consider the following two crucial components: The first key component is the fast handling of huge microscopy data. Electron microscopes can nowadays acquire several GBs of data within seconds which, means that ML methods for real-time applications should be capable of processing huge data sets within a fraction of a second. Obviously, a tight integration between hardware and software will play a crucial part in the solution to this problem. Edge computing and camera integrated compression techniques are here just two examples to be mentioned. Another important component for the development of new real-time ML methods is a high level of adaptability. The environment in the microscope constantly changes between, but sometimes even during experimental sessions. ML methods need to deal in real-time with data that has been acquired under these circumstances without a significant loss in performance. Furthermore, methods that aim for an automation of the experiment are required to easily adapt to different experimental goals.