Navigating Data-Centric AI

This chapter examines the distinctions between data-centric AI and traditional methodologies, highlighting the importance of tailored practices for different data types like vision, text, and audio. It stresses the significance of robust data engineering and strategic investments in data architecture for businesses aiming to harness AI technologies effectively. The discussion also covers the role of agentic workflows in optimizing AI performance while addressing challenges related to the curation of machine-generated content.