Today, data is a first-class input to AI-powered applications alongside code and configuration. However, many AI development practices are ad-hoc. To achieve end-to-end guarantees on lineage, integrity, and safety of models, one must adopt a Data-as-Code (DaC) approach. This session will explore the tenets of DaC from VCS to training to storage to serving and implementing.