Metric learning aims to train models that can embed inputs into a high-dimensional space such that \"similar\" inputs, as defined by the training scheme, are located close to each other. These models once trained can produce embeddings for downstream systems where such similarity is useful; examples include as a ranking signal for search or as a form of pretrained embedding model for another supervised problem.