Most engineers can build AI applications. Very few engineers can design AI systems that scale. As AI adoption grows, companies need engineers who understand not just models and APIs, but also the architecture, scalability, reliability, and infrastructure behind production AI systems. This course focuses entirely on AI System Design and teaches how modern AI products are architected, scaled, and optimized in real world environments.
Through practical case studies, you will learn how technologies such as LLMs, RAG, AI Agents, Kafka, Redis, Kubernetes, Vector Databases, FastAPI, and Databricks work together to power enterprise AI applications. You will also learn architecture patterns used in Machine Learning, Supervised Learning, Unsupervised Learning, Semi Supervised Learning, Natural Language Processing, and Computer Vision systems.
Whether you are transitioning into AI Engineering, preparing for AI System Design interviews, or building AI powered products, this course will help you think like a Senior AI Engineer and AI Architect.
