Real-time generative AI applications promise transformative user experiences—but scaling inference in production is a constant battle. Demand spikes, infrastructure meltdowns, and unpredictable model behaviors lurk behind every release.

Drawing from direct experience deploying mission-critical AI workloads at startups and large enterprises, this talk covers the four essential components of a production-ready inference platform: applied model performance research, distributed multi-cloud infrastructure, model management and observability tooling, and AI engineering expertise. I’ll highlight common pitfalls and share concrete advice on how to build robust, resilient systems that thrive under real-world demand.