Tigris Blog

Discover how to efficiently train large-scale ML models using LanceDB and Tigris. This guide details streaming massive datasets directly from object storage to PyTorch, simplifying distributed training and eliminating infrastructure complexities for scalable AI.

Training AI models on Big Data can be challenging due to the need for flexible storage and compute. Tigris, a cloud-agnostic storage layer, enables decoupling of storage from compute, making it easier to manage data and model weights across clouds. Tools like SkyPilot that abstract cloud providers and operating system configuration simplifies the compute layer, allowing for seamless data transfer between clouds using Tigris.