Low AWS GPU usage? Achieve up to 95% GPU utilization in SageMaker with Hub

This case study demonstrates how teams can maximize GPU utilization and minimize unnecessary data copies using Amazon's cloud infrastructure without running into IO issues or sacrificing GPU utilization. It combines SageMaker with Hub, which formats datasets for deep learning projects and fetches data from a remote source, applies transformations, creates batches, and sends it to a local GPU. By storing unstructured data in Hub format (chunked, compressed arrays on the cloud), practitioners can get the best of both worlds: store arbitrarily large datasets on cloud object stores, benefiting from infrastructure scale, code maintainability, and data accessibility while also accessing data with high throughput.