Artificial Intelligence as a Service (AIaaS) for Cloud, Fog and the Edge: State-of-the-Art Practices

Abstract

Artificial Intelligence (AI) fosters enormous business opportunities that build and utilize private AI models. Implementing AI models at scale and ensuring cost-effective production of AI-based technologies through entirely in-house capabilities is a challenge. The success of the Infrastructure as a Service (IaaS) and Software as a Service (SaaS) Cloud Computing models can be leveraged to facilitate a cost-effective and scalable AI service paradigm, namely, ‘AI as a Service.’ We summarize current state-of-the-art solutions for AI-as-a-Service (AIaaS), and we discuss its prospects for growth and opportunities to advance the concept. To this end, we perform a thorough review of recent research on AI and various deployment strategies for emerging domains considering both technical as well as survey articles. Next, we identify various characteristics and capabilities that need to be met before an AIaaS model can be successfully designed and deployed. Based on this we present a general framework of an AIaaS architecture that integrates the required aaS characteristics with the capabilities of AI. We also compare various approaches for offering AIaaS to end users. Finally, we illustrate several real-world use cases for AIaaS models, followed by a discussion of some of the challenges that must be addressed to enable AIaaS adoption.