AI and machine learning efforts need access to centralized, high-quality data. However, managing data pipelines in-house often leads to complexity, inefficiencies, and delays that stall AI innovation. This session explores how automation transforms data movement—streamlining ingestion, normalization, and preparation—to fuel AI/ML applications effectively. Discover how automated pipelines, managed data lake services, and rapid deployment models empower teams to move from experimentation to production faster, unlocking real business value. Join this session to learn: Strategies to centralize and normalize data from diverse sources How to get reliable, high-quality data from automated pipelines Ways to accelerate AI from experimentation to production with scalable data movement