TGS deployed a Vision Transformer-based Seismic Foundation Model (SFM) on Amazon SageMaker HyperPod achieving near-linear scaling for distributed training
Training time reduced dramatically from 6 months to just 5 days through optimized distributed training infrastructure
Expanded context windows enable analysis of larger seismic volumes than previously possible, improving model capabilities
AWS SageMaker HyperPod provided the distributed training foundation necessary for handling massive seismic datasets at scale
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