94-879 Fundamentals of Operationalizing AI
6 units, Carnegie Mellon University
Artificial Intelligence (AI) is rapidly transforming industries by driving innovation, improving efficiency, and enhancing decision-making processes. Yet, despite its potential, many AI projects face significant hurdles in deployment. According to a recent survey, only 22% of data scientists report that their "revolutionary" AI initiatives—those designed to enable new processes or capabilities—usually reach deployment. Alarmingly, 43% say that 80% or more of their AI projects fail to make it into production. Even when considering all types of machine learning projects, including those focused on refreshing existing models, only 32% of models typically deploy. These statistics underscore the critical challenges of scaling AI systems effectively. This course is designed to tackle these challenges head-on, providing graduate-level students with a comprehensive understanding of the AI lifecycle. Students will learn how to navigate the complex process of identifying which business tasks should be automated through AI and which decisions should be augmented using AI. The course introduces practical frameworks essential for making these strategic decisions and successfully implementing AI solutions. This course is essential for students aspiring to careers as AI engineers, AI analysts, or AI governance experts. It is equally invaluable for business and technology students who wish to understand how to manage the development and deployment of AI systems. By the end of the course, students will possess a well-rounded, practical understanding of AI system management, enabling them to lead AI-driven projects and drive innovation across industries.
Prerequisites: none listed
Offered: Spring 2027, Fall 2026, Spring 2026, Fall 2025
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