AI models drive several Meta products like News Feed, Ads, IG Reels, language translation to name a few. Our ranking models consume massive datasets to continuously improve user experience on our platform. In this talk, we discuss our experience of building infrastructure to serve massive scale data to the 1000s of AI models driving our products. Further, we present AI training data pipeline workload characteristics and challenges in scaling these systems for industry-scale use cases. Inefficiencies in these pipelines result in expensive wasted GPU/accelerator training resources in our data centers. In this talk, we outline our experience in optimizing these data ingestion pipelines and our plans to continue innovation in this space.