Advancing Healthcare AI Education Through Cloud Computing: Benchmarking AWS vs. GCP

This research-to-practice full paper represents our work to comprehensively analyze cloud computing benchmarking with integrated AI-driven healthcare projects. As time progresses, the need to migrate a variety of system applications to the cloud has grown significantly. Yet, choosing a particular cloud service provider can be troublesome and proposes several challenges, whether computational or financial, depending on the domain of the application. Thus, the reliance on cloud platform benchmarking has become an essential skill engineers must develop in order to make informed and efficient decisions. Healthcare applications, in particular, constitute an excellent from-ground- to-cloud example that we needed to analyze and study, especially from the perspective of Artificial Intelligence (AI) applications. At the moment, there is high demand for AI applications in the domain of healthcare seeking higher performance while lowering the costs of the services. Nonetheless, the healthcare domain is considered a tough field to experience and experiment to new graduates and proposes challenges to successfully cloudify the designated system applications. Therefore, we developed an effectual pedagogical approach capable of addressing those stacked challenges and preparing a new generation of competent engineers. Our work is designed to implement multiple stages sequentially and individually. The first stage commences by introducing students to various concepts as a foundation for their core work, covering Machine Learning (ML) basics, healthcare domain knowledge basics, finding a solution via ground and cloud computation, and benchmarking. Students then attempted to address a chosen healthcare problem using AI techniques. Finally, they solve the proposed healthcare challenge and successfully benchmark the cloud platforms of both Google Cloud Platform (GCP) and Amazon Web Services (AWS). Students, as instructed, relied on concepts of metrics, measurements, criteria, and their disparities to accurately assess each cloud. This paper efficiently documents and analyzes the results of each proposed stage based on the represented students' deliverables and the supervisors' feedback to reflect the effectiveness of the suggested teaching approach.