AI-Driven Deployment and Governance

This capstone unit synthesizes project lifecycle phases and governance practices required to deploy, transition, monitor, and close AI-driven projects. It emphasizes scalability, ethical/privacy and accessibility considerations, regulatory compliance, and choosing appropriate predictive or hybrid development approaches to prepare learners for real-world implementation and continuous improvement.

Learning Objectives

• Analyze the initiating, iterative (sprint/cycle), monitoring & controlling, release/transition, deployment, and closing phases to recommend an appropriate predictive, iterative, or hybrid project approach for a given AI-driven project scenario
• Apply ethical, privacy, accessibility, scalability, and regulatory considerations to create a first-time AI implementation plan that includes mitigation strategies, compliance checkpoints, and inclusive design enhancements
• Demonstrate the ability to design monitoring and controlling artifacts (KPIs, sprint backlogs, release and rollback plans, transition checklists) and execute a deployment sequence that ensures safe transition to production
• Evaluate project outcomes and produce a closing and post-deployment evaluation report with lessons learned, improvement recommendations, and a scaling roadmap for educational or business contexts