Shipping an AI system is one thing. Operating it across teams, products, and regions is another.  

This session looks at what it takes to run AI as a long-term capability inside an organization. We explore operating models, ownership structures, and workflows that support reliability, accountability, and continuous improvement as AI usage grows.  

We also examine how traditional DevOps practices evolve into MLOps and AgentOps as AI becomes embedded across the development lifecycle.  

What you will learn 

• Organizational models for owning and operating AI systems  
• DevOps to MLOps to AgentOps evolution  
• Skills, roles, and responsibilities at scale  
• Designing operating models that support sustainable growth  

This session is for technical leaders and engineers responsible for AI beyond a single team or project.