Amazon SageMaker geospatial capabilities now support Notebook with GPU-based Instances
You can now use the geospatial image within SageMaker Studio’s notebook with GPU-based instances. Support for GPU-based instances with the geospatial image makes it easier for data scientists and machine learning (ML) engineers to build, train, and deploy ML models using geospatial data.
You can now use the geospatial image within SageMaker Studio’s notebook with GPU-based instances. Support for GPU-based instances with the geospatial image makes it easier for data scientists and machine learning (ML) engineers to build, train, and deploy ML models using geospatial data.
Customers use the geospatial image within SageMaker Studio Notebooks to develop and run end-to-end geospatial ML workloads. Customers need the geospatial image to access a curated geospatial data catalog, open-source geospatial libraries to perform custom analysis, open-source or pre-trained models for ML training or inference, and purpose-built visualization tools to visualize predictions on a map and collaborate with other team members. Now, with GPU-based instances, customers can train models or make predictions, so they can accelerate development of geospatial ML workloads.
Support for the geospatial image within SageMaker Studio notebook for GPU-based instances is now Generally Available in US West (Oregon) Region.
To learn more about geospatial ML capabilities, visit the webpage, or view our documentation.