Building AI models is easy. Making them matter in the real world is hard. Too often, organizations become stuck in endless pilots: brilliant models that never scale, systems that collapse under real-world data, and innovation that is slowed by fragile infrastructure.

This session explores why cloud-native principles are the missing link between AI experiments and AI impact — and how Microsoft Azure provides the foundation to make this a reality. The audience will see how Azure Container Apps Serverless GPUs let teams run AI inference on demand without managing infrastructure, and how dynamic sessions provide secure, sandboxed compute for LLM-generated code, AI agents, and ephemeral workloads. I will also examine how Azure AI Foundry integration simplifies model deployment directly to Container Apps, as well as how sidecars in Azure App Service make AI-native apps easier to deploy, scale, and manage.

Through practical examples, the audience will learn how these capabilities turn models into production-ready systems that adapt, evolve, and deliver value with efficiency and resilience. Whether you’re building generative AI solutions or scaling intelligent apps, this talk will show why cloud-native on Azure isn’t optional for AI — it’s essential.