Generative AI is transforming enterprises, but moving from prototype to production requires a new operational discipline. This Professional Certificate teaches Microsoft's GenAIOps framework: the practices, tools, and architectures needed to deploy, monitor, and govern generative AI at enterprise scale on Azure.
You'll design end-to-end GenAIOps operating models integrating DataOps for grounding data, PromptOps for versioning and experimentation, RAG pipeline optimization, and automated evaluation frameworks. You'll build CI/CD pipelines with progressive deployment, production observability, cost governance, content safety configurations, and continuous improvement loops.
Hands-on activities use Microsoft Foundry, Azure OpenAI Service, and Azure API Management to configure real enterprise environments. Each course includes design artifacts—architecture diagrams, runbooks, policies, and cost models—building a portfolio that demonstrates your ability to design production-grade GenAI systems.
You'll be prepared to implement and operate production GenAIOps systems that are reliable, safe, cost-effective, and continuously improving.
Who this is for: ML engineers, MLOps practitioners, platform engineers, and cloud engineers with foundational cloud/ML experience who want to specialize in operationalizing generative AI on Azure. Activities require an Azure subscription with Microsoft Foundry access; enterprise access is expected.
Projet d'apprentissage appliqué
Throughout this program, you'll complete four hands-on projects that build your GenAIOps portfolio. You'll design a DataOps architecture for a RAG solution serving a legal firm, including grounding data pipelines, index management, and data lineage. You'll build a prompt and RAG experimentation framework with automated evaluation gates. You'll architect a complete CI/CD and progressive deployment pipeline for an enterprise AI platform, including observability and monitoring infrastructure. Finally, you'll create an integrated GenAIOps operating model covering governance, FinOps, content safety, and incident response procedures. Each project produces documentation artifacts—architecture diagrams, runbooks, policy documents, and operating models—suitable for demonstrating your expertise to employers.
