The text discusses the importance of feature stores extending, rather than replacing, existing data infrastructure. It highlights the limitations of isolated ML project stacks and the benefits of integrating feature stores with underlying data platforms to centralize and make available ML data. The author emphasizes that ML has unique requirements, but also shares common non-unique requirements, which should be addressed by optimal ML data infrastructure design. Feature stores aim to maximize the use of existing platform and infrastructure while supporting the best workflows for AI/ML developers. The text concludes with a call to attend Apply(conf), a main event for the year, featuring speakers working on fascinating work in ML data engineering.