FrontierThe story, in brief
Scaling seismic foundation models on AWS: Distributed training with Amazon SageMaker HyperPod and expanding context windows
6 months to 5 days. That's how TGS cut AI training time using AWS SageMaker HyperPod for seismic analysis.
The KeyNews take
Why it matters
This demonstrates how enterprise AI infrastructure can dramatically accelerate specialized foundation model training, making previously impossible large-scale analysis feasible for energy sector applications.
The key facts
5 to knowTraining time reduced from 6 months to 5 days
Near-linear scaling achieved for distributed training
Expanded context windows enable analysis of larger seismic volumes
Vision Transformer-based Seismic Foundation Model (SFM)
Amazon SageMaker HyperPod infrastructure
Go to the source
AWS Machine Learning Blogaws.amazon.com
Publisher excerpt: This post describes how TGS achieved near-linear scaling for distributed training and expanded context windows for their Vision Transformer-based SFM using Amazon SageMaker HyperPod. This joint solution cut training time from 6 months to just 5 days while enabling analysis of seismic volumes larger…