04-652 Artificial Intelligence System Design
12 units, Carnegie Mellon University
In a world where classical approaches increasingly fall short of the demands placed on modern intelligent systems, responsible AI-driven solutions have become essential. This course guides students through the process of transforming real-world problems into working AI systems that are effective, reliable, scalable, and responsible. Students learn core principles of machine learning, deep learning, and data workflows, along with best practices for model development, evaluation, and deployment. Throughout the course, students develop the ability to design robust data pipelines, select appropriate models, and anticipate system behavior in real-world contexts. By the end of the course, students are equipped to build AI systems that are both technically sound and ethically grounded. A strong emphasis is placed on design trade-offs, including: - Lightweight vs. heavy models - Accuracy vs. latency - Scalability vs. resource constraints Students learn to justify architectural decisions based on context, ensuring their AI solutions are practical, optimized, and deployable.
Prerequisites: none listed
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