Mobile networks like 4G or 5G networks support mobile users, necessitating, e.g., handover and allocation of scarce resources (with radio spectrum, storage, data rate, ... all being relevant resources). Also, these networks operate at huge scales, with millions of users accessing hundreds of thousands of basestations in a country. Conventional optimization approaches have been well investigated, but keeping up with rapidly changing demands, user preferences, is challenging. Hence, looking at machine learning approaches for resource management is promising and considered a key ingredient for 6G networks, leading to self-driving networks. Also, conversely, using machine-learning-based applications inside mobile networks with their inherently volatile infrastructure is an increasing challenge.