Designing Generative AI Solutions on AWS: Requirements and Patterns

Building generative artificial intelligence applications requires more than just calling an API; it demands a clear understanding of business needs and architectural trade-offs. This course guides you through translating business requirements into robust, scalable cloud architectures. You will learn to evaluate project constraints and select the optimal AWS design patterns for your generative AI workloads. Through structured text-based lessons and written scenarios, you will gain the skills to design cost-effective, high-performing, and secure AI systems. What you'll learn: 1. Analyze business requirements to define key performance, latency, and cost metrics. 2. Understand the differences between synchronous, asynchronous, and batch inference patterns on AWS. 3. Apply Retrieval-Augmented Generation (RAG) patterns to ground model outputs with external data sources. 4. Evaluate proof-of-concept patterns to rapidly validate generative AI ideas. 5. Design secure and observable architectures that align with cloud best practices. The course begins with foundational definitions of generative AI infrastructure and requirement gathering. You will then progress through detailed written analyses of specific architectural patterns, complete with conceptual design exercises to reinforce your learning. This course is designed for aspiring cloud architects, developers, and technology coordinators who are new to generative AI on AWS. No prior cloud engineering experience is required. Start reading today to master the architectural patterns that drive successful generative AI deployments.