The Tecton apply() conference discussed various themes related to machine learning and data engineering, including productionization, data architectures for machine learning applications, real-time machine learning, rapid model deployment in healthcare, best practices for scaling a company's data team, and improving collaboration between engineering and data teams. The event highlighted the challenges of putting good models into production, the importance of unified data models, and the need for faster iteration cycles and better ways to manage data. It also showcased successful real-time machine learning applications, such as CashApp's personalization experiences and ERAdvisor's emergency room wait time estimates. Additionally, speakers shared best practices for scaling data teams and improving collaboration between engineering and data teams as companies mature. The next event, apply(recsys), will take place on December 6 and will focus on recommender systems.