Enabling AI: Cloud Platforms, Data Architecture, and Scalable Systems

1 Unit · Graduate

As artificial intelligence (AI) becomes foundational to modern healthcare and biomedical research, scalable infrastructure and cloud computing environments are critical to support real-time analytics, machine learning pipelines, and growing healthcare datasets. This course introduces the key concepts, tools, and architectures that underpin AI-readiness at scale, with a focus on practical applications in health systems, research institutions, and AI innovation hubs. Students will gain hands-on exposure to core components of modern data infrastructure, including cloud service models (IaaS, PaaS, SaaS), containerization (Docker, Kubernetes), data pipelines, and storage strategies optimized for protected health information (PHI). The course also explores the role of distributed computing, high-performance environments, and MLOps practices for deploying AI models into production. Through real-world case studies and guided design exercises, students will assess trade-offs in cost, compliance, scalability, and performance, developing a systems-thinking approach to supporting AI and data science initiatives in clinical, operational, and research settings.