AI Product Operations and Ethics

This unit synthesizes prior learning to prepare students to take AI systems from research into production responsibly and at scale. Students examine case studies, compare generative and traditional AI trade-offs, integrate MDLC and SDLC practices into product roadmaps, and address explainability, drift, bias, ethics, and model operations as part of an intelligence-first UX.

Learning Objectives

• Analyze trade-offs between generative and traditional AI across the MDLC and SDLC, including explainability, interpretability, performance, drift, and user experience implications.
• Apply integrated MDLC and SDLC strategies to design a model-to-production roadmap that specifies deployment, monitoring, model operations (MLOps), and scaling approaches with measurable milestones.
• Evaluate case studies of model underperformance, ethical failures, and bias to identify root causes and propose concrete mitigation plans with defined KPIs and governance controls.
• Demonstrate incorporation of model operations and intelligence-first UX improvements into product requirements and stakeholder communications through a documented plan or prototype.