PostgreSQL for AI-Driven Applications: Performance Tuning for Machine Learning Pipelines

Abstract

In the context of today's AI, data handling constitutes one of the cornerstones of modern ML operations. Due to the fact that databases serve as information storage units of such AI-driven systems, their performance and scalability determine the speed and reliability of such systems. The efficient open-source relational database management system is PostgreSQL, which is used more often as the database for AI applications due to its sophisticated indexing, JSON support, and analytics features, such as PostgreSQL-ML. This work thoroughly analyzes tables required for PostgreSQL to optimize various aspects of machine learning processing. Specifically, by examining Query Performance through execution plans, Data In Wild in Handling, Thread Parallelism, and GPU Extensions, this paper will establish how to turn PostgreSQL into an efficient Data Platform for Deep Learning and Artificial Intelligence. The experiments with the datasets from healthcare, finance, and e-commerce domains have shown that by tuning and optimizing settings and architecture, PostgreSQL can be on par with the specific data engines in terms of training time and model quality. We also provide a tuning guide with best practices and scripts that data engineers can use to simplify the process. This paper benefits the field by presenting several findings, standard reference points, and architectural suggestions for integrating PostgreSQL as a large-scale AI platform.