Operationalizing AI Products

This capstone unit synthesizes prior learning to prepare students to take AI prototypes into production, focusing on integrating the MDLC and SDLC, model operations, and real-world trade-offs between generative and traditional AI. Through case studies and applied planning, learners address explainability, ethics, bias, model drift, and product roadmap decisions required for scalable, intelligence-first products.

Learning Objectives

• Analyze trade-offs between generative and traditional AI approaches for specific product requirements, explicitly comparing explainability, interpretability, ethics, bias risk, performance, and scalability, and justify model choice for given use cases.
• Evaluate strategies for integrating the Model Development Life Cycle (MDLC) with the Software Development Life Cycle (SDLC) and propose a product roadmap that incorporates MLOps, monitoring, rollback, and mitigation plans to manage model drift and underperformance.
• Apply model operations best practices by designing concrete deployment, monitoring, and incident-response plans that specify metrics, thresholds, alerting, and remediation steps for data shifts, performance degradation, and ethical or bias incidents.
• Demonstrate synthesis of multiple real-world case studies to produce an intelligence-first product plan that addresses UX impacts, regulatory and ethical considerations, scaling from research to production, and measurable success criteria.