Preliminary abstract: I will highlight the role of algebraic geometry in enhancing our understanding of machine learning models, offering a fresh perspective on the problem of neural network verification. I will discuss an approach for ensuring the behavior of a ReLU network at test time by establishing systematic algebraic relations that are satisfied by the outputs produced at various data points. I will emphasize the combinatorial and geometric properties of these relations and explain how they constrain the possible output values at test points of interest. This is joint work with Guido Montúfar.