Scaling generative AI is the defining challenge for technology leaders today. The gap between a successful proof of concept and a production-grade enterprise system is not a technical barrier -it is an engineering and governance challenge that demands new disciplines, new tooling patterns, and new financial controls.
In this advanced course, you will develop the frameworks and decision-making protocols needed to architect, operate, and govern generative AI systems at enterprise scale. Working through the five-domain GenAI Scaling Framework -Deployment Architecture, RAG, MLOps, Cost Optimisation, and Governance -you will build the capability to lead a production-ready GenAI deployment from first principles.
Each module follows a single enterprise scenario: GlobalScale Inc., a financial services and logistics firm scaling a legal AI agent from a 200-user pilot to a 12,000-user global deployment. You will diagnose real scaling failures, document architecture decisions, and build the components of a Production Readiness Assessment that serves as your capstone deliverable.
Throughout the course, you will work with Microsoft Azure AI Foundry, Azure OpenAI Service, Azure AI Search, and Azure Machine Learning -the platform architecture that underpins enterprise GenAI deployments across Microsoft's ecosystem. By the end of the course, you will have produced a structured set of Architecture Decision Records and a completed Production Readiness Assessment that you can adapt for your own organisation.
