Operationalizing Intelligence-First Products

This capstone unit synthesizes MDLC and SDLC integration, model operations, and the practical tradeoffs between generative and traditional AI when scaling from research to production. Through case studies and applied exercises learners prepare operational roadmaps that address explainability, ethics, bias, drift, monitoring, and recovery to lead AI-driven product initiatives.

Learning Objectives

• Analyze case studies of MDLC and SDLC integration to identify and prioritize at least three actionable practices for scaling AI systems that address explainability, ethics, bias, and model drift
• Apply integrated MDLC+SDLC frameworks to develop a model operations roadmap for a hypothetical product that balances generative and traditional AI components, including monitoring, rollback, and update triggers
• Demonstrate techniques for managing model explainability, interpretability, and ethical risk by producing mitigation plans and stakeholder communication templates for both generative and traditional models
• Evaluate post-deployment performance and recovery strategies by critiquing real-world examples of initial underperformance and proposing a prioritized plan for operational adjustments and UX improvements