Practical Lessons from Building AI Infrastructure for Billion User Products

This talk covers what it takes to move ML/AI systems from promising prototypes to production systems that are reliable, observable, scalable, and maintainable. I will discuss common architecture patterns, rollout strategies, evaluation and monitoring loops, operational failure modes, and engineering tradeoffs that show up at large scale. The talk is intended for experienced computing professionals

Speaker: Silu Panda, Linkedin

Attend in person or online.  See weblink