The relational data model was designed to facilitate large-scale data management and analytics. We consider the problem of how to differentiate computations expressed relationally. We show experimentally that a relational engine running an auto-differentiated relational algorithm can easily scale to very large datasets, and is competitive with state-of-the-art, special-purpose systems for large-scale distributed machine learning.
@article{arxiv.2306.00088,
title = {Auto-Differentiation of Relational Computations for Very Large Scale Machine Learning},
author = {Yuxin Tang and Zhimin Ding and Dimitrije Jankov and Binhang Yuan and Daniel Bourgeois and Chris Jermaine},
journal= {arXiv preprint arXiv:2306.00088},
year = {2023}
}