AI Solution Architecture: Designing Systems from Needs to Models

Building successful AI systems requires more than just training models; it demands a clear strategy to align technical architecture with actual business needs. This course teaches you how to bridge that gap, transforming high-level goals into concrete, structured AI designs. You will progress from understanding core AI terminologies to designing end-to-end solutions. You will learn to evaluate business requirements, select appropriate model families, and map out data flows, ensuring your AI initiatives are both technically viable and strategically aligned. What you'll learn: 1. Understand foundational AI concepts, system components, and architectural terminology. 2. Translate business objectives into clear technical requirements. 3. Evaluate and select the right AI models, from traditional machine learning to modern large language models. 4. Design robust data flows incorporating vector databases and retrieval-augmented generation (RAG) patterns. 5. Configure basic MLOps workflows to ensure model monitoring and scalability. 6. Analyze real-world case scenarios to practice mapping business needs to technical architectures. The curriculum starts with essential definitions and architectural principles before guiding you through model selection, data pipelines, and deployment strategies. You will read comprehensive explanations and practice your skills through written design exercises. This course is designed for aspiring AI architects, product managers, and business analysts looking to enter the AI space. No prior programming or advanced mathematics experience is required. Start reading today to master the fundamentals of modern AI system design.