Managing AI Systems: Development, Deployment, and Governance

About this course

This three-course specialization moves beyond high-level theory to the gritty reality of managing modern AI stacks. It equips AI Product Managers, Technical Program Managers, Innovation Leads, and Governance Officers to handle the shift from deterministic software to probabilistic AI systems — navigating the trade-offs between model performance, inference costs, and safety to ensure AI initiatives survive the transition from Proof of Concept to production. You will begin by architecting AI solutions using orchestration frameworks, vector databases, and RAG pipelines, building a functional chatbot MVP with LangChain, ChromaDB, and Streamlit. The second course shifts to production operations — mastering LLMOps workflows, prompt versioning, evaluation strategies including LLM-as-a-Judge metrics, and observability using tracing and drift monitoring tools. The final course prepares you to enforce safety and compliance through Red Teaming, guardrails implementation, explainability techniques, and regulatory navigation. By the end, you will be able to architect, operationalize, and govern AI systems with the rigor required for enterprise-scale deployment.

Course objectives

• prepare professionals to manage the transition of AI from concept to production
• provide skills in monitoring and evaluating AI system performance
• teach enforcement of regulatory requirements for AI systems