Shipping a generative AI feature is only the start. Keeping it reliable under production load is the real challenge. This session brings together ML and platform engineers to discuss how they build and maintain production-ready genAI systems. Topics include managing model drift, optimizing inference performance, strengthening orchestration, and reducing infrastructure costs. Key takeaways: → Engineering methods for stabilizing GenAI pipelines under real-world conditions. → How to monitor, evaluate, and version models to control drift and degradation. → Architecture patterns that improve reliability, scalability, and cost efficiency in production.