On-Premise Artificial Intelligence as a Service for Small and Medium Size Setups

TL;DR

This paper explores how open-source AI as a Service can be implemented on-premise for small and medium-sized organizations, enabling data control and avoiding vendor lock-in.

Contribution

It analyzes open-source AIaaS technology stacks suitable for on-premise deployment in small and medium enterprises, emphasizing data privacy and independence.

Findings

Open-source AIaaS can be effectively deployed on-premise for small and medium setups.

Such solutions provide full control over data without third-party dependence.

The paper identifies suitable open-source tools and architectures for these deployments.

Abstract

Artificial Intelligence (AI) technologies are moving from customized deployments in specific domains towards generic solutions horizontally permeating vertical domains and industries. For instance, decisions on when to perform maintenance of roads or bridges or how to optimize public lighting in view of costs and safety in smart cities are increasingly informed by AI models. While various commercial solutions offer user friendly and easy to use AI as a Service (AIaaS), functionality-wise enabling the democratization of such ecosystems, open-source equivalent ecosystems are lagging behind. In this chapter, we discuss AIaaS functionality and corresponding technology stack and analyze possible realizations using open source user friendly technologies that are suitable for on-premise set-ups of small and medium sized users allowing full control over the data and technological platform without any third-party dependence or vendor lock-in.