Architecting scalable checkpoint storage for large-scale ML training on AWS
The exponential growth in size and complexity of foundation models (FMs) has created unprecedented infrastructure demands across compute, networking, and storage resources. Storage systems, in particular, face intense requirements for throughput, latency, and capacity. In machine learning (ML) model training, these storage demands are particularly evident in checkpointing—a critical reliability mechanism that periodically saves and […]