Metric learning aims to train models that can embed inputs into a high-dimensional space such that \"similar\" inputs are pulled closer to each other and \"dissimilar\" inputs are pushed farther apart. Once trained, these models can produce embeddings for downstream systems where such similarity is useful, for instance as a ranking signal for search or as a form of pretrained embedding model for another supervised problem.