Operationalizing AI: From Prototype to Production

Building an AI model is the easy part—making it work reliably in production is where the real engineering begins. In this fast-paced, experience-driven session, Ken explores the architecture, patterns, and practices behind operationalizing AI at scale. Drawing from real-world lessons and enterprise implementations, Ken will demystify the complex intersection of machine learning, DevOps, and data engineering, showing how modern organizations bring AI from the lab into mission-critical systems.

Attendees will learn how to:

Design production-ready AI pipelines that are testable, observable, and maintainable

Integrate model deployment, monitoring, and feedback loops using MLOps best practices

Avoid common pitfalls in scaling, governance, and model drift management

Leverage automation to reduce friction between data science and engineering teams

Whether you’re a software architect, developer, or engineering leader, this session will give you a clear roadmap for turning AI innovation into operational excellence—with the same pragmatic, architecture-first perspective that Ken is known for.