This course explores how AI systems are built, deployed, and operated in cloud environments using modern practices. Students follow the lifecycle of an AI service from initial design through cloud deployment, monitoring, governance, and scale. The emphasis is on understanding how infrastructure, policy, organizational constraints, and leadership decisions shape real-world AI adoption—and on developing the judgment to make and defend those decisions.
The course is designed for students with a mix of technical and business backgrounds. Rather than requiring deep software engineering skills, it focuses on the decisions, tradeoffs, and operational thinking that managers, analysts, and technical leads need when bringing AI systems into production. Students will write Python scripts, call APIs, use managed cloud services, and work with AI coding assistants.
The course is built around designing, building, and operationalizing an AI-enabled service for an institution, using La Roche as the reference environment. Each four-hour session is split into two halves—a lecture and concepts portion followed by a hands-on lab—so that every idea is immediately applied.