Designing Scalable Cloud AI Infrastructure

Scaling AI models in the cloud requires more than just raw computing power; it demands a strategic approach to infrastructure design that keeps costs manageable and systems reliable. This text-based course guides you through the fundamental principles of architecting cloud systems specifically tailored for AI workloads. You will transition from understanding basic cloud concepts to confidently planning robust architectures that support machine learning pipelines, large language models, and real-time data processing. What you'll learn: Understand the foundational hardware and software components of modern cloud AI infrastructure; Design scalable data pipelines and integrate vector databases for retrieval-augmented generation (RAG); Configure compute resources, including GPUs and specialized accelerators, to balance performance and operational costs; Implement basic MLOps workflows for model deployment, monitoring, and automated scaling; Apply best practices for cost control, security, and high availability in enterprise cloud environments. We begin with key terminology and core cloud architectural concepts before exploring practical design patterns for model training and serving. Through detailed written explanations and conceptual breakdowns, you will learn how to make informed decisions about storage, compute, and networking. This course is designed for aspiring cloud architects, system administrators, and developers looking to specialize in AI infrastructure, with no prior cloud architecture experience required. Start building your foundational knowledge of cloud AI design today.