Incorporating Deep Learning Design in Database Queries
Researchers have developed a new method to integrate deep learning directly into database queries, bypassing the need for external ML frameworks. This approach associates each database tuple with a learnable vector embedding, allowing queries to operate on both data and embeddings. A proof-of-concept implementation called RelaNN, built on PyTorch and cuDF, demonstrates the feasibility of this declarative foundation for relational deep learning. AI