Navigating AutoML and Data Governance

This chapter explores the evolution of AutoML, highlighting the tension between traditional data science and automated techniques. It emphasizes the importance of transparent storytelling and collaboration across teams to improve model understandability and implementation. Additionally, the discussion addresses challenges in data anonymization and the role of federated learning in enhancing data privacy while enabling effective data sharing.