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Vertica-ML: Distributed Machine Learning in Vertica Database

27 Citations2020
A. Fard, Anh Le, George Larionov
Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data

This paper presents their distributed machine learning subsystem within the Vertica database, Vertica-ML, which includes machine learning functionalities with SQL API which cover a complete data science workflow as well as model management and a set of experiments to evaluate the performance of the machine learning algorithms implemented on top of it.

Abstract

A growing number of companies rely on machine learning as a key element for gaining a competitive edge from their collected Big Data. An in-database machine learning system can provide many advantages in this scenario, e.g., eliminating the overhead of data transfer, avoiding the maintenance costs of a separate analytical system, and addressing data security and provenance concerns. In this paper, we present our distributed machine learning subsystem within the Vertica database. This subsystem, Vertica-ML, includes machine learning functionalities with SQL API which cover a complete data science workflow as well as model management. We treat machine learning models in Vertica as first-class database objects like tables and views; therefore, they enjoy a similar mechanism for archiving and managing. We explain the architecture of the subsystem, and present a set of experiments to evaluate the performance of the machine learning algorithms implemented on top of it.