AI systems often work well during development but struggle when they are deployed at scale across real users, data, and infrastructure. In this session, I will share lessons learned from building AI and full-stack systems that operate under heavy load and strict performance requirements.

We will look at common failure points such as slow data pipelines, hidden bottlenecks, poor system boundaries, and limited visibility into runtime behavior. I will also discuss how modern AI workloads increasingly interact with other software ecosystems like Python, Rust, and service-based applications, and why these integrations often introduce performance and reliability challenges.

The session focuses on system-level thinking rather than models alone. Using practical examples, I will explain how open source tools, thoughtful architecture choices, and better observability can help teams design AI systems that scale responsibly. Attendees will leave with clear guidance on how to identify risks early and build AI systems that perform well in real-world, high-performance environments.