Enterprise AI development has primarily been model-centric over the past decade. Data Scientists have benefited from advances in algorithms, AutoML, and MLOps tools for developing, deploying, and monitoring models. Yet, data science remains challenging and highly specialized. And scaling AI remains a distant dream for most enterprises. Ask any data scientist, and they will tell you why. The process of preparing, deploying, managing, and monitoring data that feeds into these models, or “Feature Engineering”, is manual and very time-consuming. It requires the confluence of three unique skills – domain knowledge, data science, and data engineering. Even in organizations with mature AI practices, these areas of expertise live in silos. And at the intersection of these silos lies a ton of friction. At FeatureByte, our team has lived this problem, working with hundreds of enterprises on their AI journeys. We believe scaling feature engineering is a necessary step to achieving AI scale. We’re building a data-centric AI solution that radically simplifies feature engineering for data scientists.