We demonstrate how machine-learning methods can inform the efficient use of these limited resources while accounting for real-world concerns, such as gaming the system and institutional constraints. Here, we predict the likelihood of a facility failing a water pollution inspection and propose alternative inspection allocations that would target high-risk facilities. Implementing such a data-driven inspection allocation could detect over seven times the expected number of violations than current practices.