Both machine learning research and philosophy of model-based science appeal to representations. Given this shared emphasis, it seems natural to conclude that we should understand the epistemic worth of deep learning models in science in terms of their capacity to represent the world. I argue that this is a mistake. I distinguish the prevailing notion of representation used in machine learning research from the concept of scientific representation that figures in the philosophy of model-based science. I argue that the former cannot do the work of the latter in model-based inferences. I then defend an artifactualist approach to machine learning models in science, which aims to understand their epistemic worth in terms of the material practices of constructing and applying them. Machine learning models are instruments that we use to facilitate our epistemic activities in science. They do so without scientific representation.