The transformative power of learning lies in automating the design of complex systems, allowing us to go from data to operation with little to no human intervention. Today, however, learning does not incorporate requirements organically, which has led to data-driven solutions prone to tampering, unsafe behavior, and biased, prejudiced actions. To realize its autonomous engineering potential, we must develop learning methods capable of satisfying statistical requirements beyond the training data. In this talk, I will go over some recent developments on the theoretical underpinnings of constrained learning to show how it is possible to learn under requirements. I will focus on algorithmic aspects, showing a practical learning rule that under mild conditions can tackle constrained learning tasks by solving only unconstrained empirical risk minimization (ERM) problems, a duality that holds despite the lack of convexity. I will illustrate how these advances directly enable the data-driven design of trustworthy systems using an example from robust learning. My goal is to introduce a practical learning tool that can be used to tackle multiple learning problems and showcase how we can go beyond the current objective-centric learning paradigm towards a constraint-driven learning one.