Auto-Differentiation of Relational Computations for Very Large Scale Machine Learning

TL;DR

This paper demonstrates that auto-differentiated relational algorithms can efficiently scale to large datasets and compete with specialized systems in large-scale distributed machine learning.

Contribution

It introduces methods for differentiating relational computations and shows their scalability and competitiveness in large-scale machine learning tasks.

Findings

Relational engines can scale to very large datasets.

Auto-differentiated relational algorithms are competitive with specialized systems.

The approach facilitates large-scale data analytics and machine learning.

Abstract

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.