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 ...
