The prospects of QML remain dampened by considerable technical challenges. A particularly significant issue is that generic QML models suffer from so-called barren plateaus in their training landscapes: large regions where cost function gradients vanish exponentially in the number of qubits employed, rendering large models effectively untrainable. A leading strategy for combating this effect, geometric quantum machine learning, is to build problem-specific models which take into account (for example) the symmetries of their data in order to focus on a smaller, relevant subset of Hilbert space.