Over the past 40 years, databases have evolved multiple times to work well for structured data. With the growth of computer vision data, we need solutions specifically optimized for these use cases. In this talk, Davit Buniatyan presents the Database for AI, a data-centric framework resolving common AI data bottlenecks. Learn how by being data-centric, you can (1) achieve up to 95% GPU utilization, and cutting down the computing spend by up to 50%, (2) collaborate on and build production-ready datasets in hours, not weeks, and (3) move 2x faster with less technical expertise within the team required.
Additionally, we will introduce an open-source framework for transforming and streaming data while training models at scale. As an outcome, you will learn how to easily create, store, version-control, and collaborate on computer vision datasets of any size. In addition, we will demonstrate an instant way to visualize, explore and query your data and integrate it with tools like SageMaker, PyTorch, or TensorFlow. In all, you will walk away with an understanding of how to build a solid data foundation with the Database for AI.