[P] Entity Embed: fuzzy and scalable Entity Resolution using Approximate Nearest Neighbors

Entity Embed is based on and is a special case of the AutoBlock model described by Amazon. It allows you to transform entities like companies, products, etc. into vectors to support scalable Record Linkage / Entity Resolution using Approximate Nearest Neighbors. Using Entity Embed, you can train a deep learning model to transform records into vectors in an N-dimensional embedding space. Thanks to a contrastive loss, those vectors are organized to keep similar records close and dissimilar records far apart in this embedding space. Embedding records enables scalable ANN search, which means finding thousands of candidate duplicate pairs of records per second per CPU.