For a long time, research in machine learning has focused almost exclusively on the development of algorithms for learning and inference. As machine learning components are now deployed in countless technical systems, various constraints arising from real-world scenarios are gaining importance and need to be considered. In software engineering, these constraints would run under the heading of non-functional properties and requirements. Constraints may be required response times, privacy and confidentiality, fairness, transparency and explainability, and safety. In the talk, I will give examples of how to address these constraints and how fulfilling multiple constraints at the same time may be solved and be a good research topic for the future.