A centralized machine learning (ML) team's purpose is to provide a unified and standardized experience for ML application development, freeing data scientists from creating new tools and processes from scratch. However, debates on ideal team structures are heating up, with some arguing that centralized ML teams are falling out of favor due to the emergence of MLOps and decentralized ML approaches. The author, who has built and scaled ML teams, argues that central ML can still be effective if done right, emphasizing the importance of a hybrid organizational structure, preemptive tooling development, and overcoming common challenges such as tooling lock, getting projects on the roadmap, and fence creation. Successful central ML teams share characteristics like having a centralized component and individual engineers on customer teams, building preemptively, and understanding customer pain points. Ultimately, the key to successful central ML lies in organizational culture, mindset, and how you build matters, with a focus on automation, standardization, and collaboration between subject matter experts.