As AI models become more capable, the focus in production environments is shifting from full automation to effective human-AI collaboration. How should humans and AI systems work together on complex tasks that neither can optimally handle alone? This 1.5-hour workshop explores the ML foundations of collaborative intelligence. We will cover concrete production patterns for three areas: determining when models should defer to humans, communicating confidence and uncertainty, and utilizing training paradigms that produce models that are better collaborators (not just better predictors). I will ground these concepts in real-world production AI use-cases.