Scaling seismic foundation models on AWS: Distributed training with Amazon SageMaker HyperPod and expanding context windows

1. 1Implement Amazon SageMaker HyperPod for distributed training
2. 2Expand context windows for Vision Transformer-based models
3. 3Optimize training data and model architecture for near-linear scaling
4. 4Monitor and adjust training parameters for optimal performance

Data scientists and machine learning engineers on a team can benefit from this solution as it enables faster training of complex models, while DevOps teams can appreciate the scalability and efficiency gains

💡 Distributed training with Amazon SageMaker HyperPod can significantly reduce training time for complex models

💡 Near-linear scaling for seismic foundation models with Amazon SageMaker HyperPod! Training time reduced from 6 months to 5 days