Geospatial ML with Amazon SageMaker
Build, train, and deploy ML models faster using geospatial dataWhy Geospatial ML?
The video shows how geospatial data, such as satellite imagery, maps, and location data, can be used to innovate faster and make smart decisions across a wide variety of use cases and industries.
Why Geospatial ML?
The video shows how geospatial data, such as satellite imagery, maps,and location data, can be used to innovate faster and make smarter decisions across a wide variety of use cases and industries.
How it works
The diagram has six sections. The first section has an image representing data and is titled, “Geospatial ML with Amazon SageMaker.” In this step, you can build, train, and deploy ML models using geospatial data.
The second section has four small images representing data sources and is titled, “Access data sources.” In this step, you can access geospatial data sources, from satellite imagery to mapping and location data, or upload your proprietary data.
The third section has an image of a globe with a small gear overlaying it and is titled, “Transform and enrich data.” In this step, you can use built-in operations such as reverse geocoding, mosaicking, and data labeling to prepare your data.
The fourth section includes an image representing models, with a small computer arrow clicking on them, and is titled, “Select or train a model.” In this step, you can select state-of-the-art, purpose-built models or bring and train your own model.
The fifth section has an image of a rocket flying out of a box and is titled “Deploy a model.” In this step, you can deploy and host your model to make predictions using real-world data.
The sixth and final section includes an image of a magnifying glass over a map and is titled, “Visualize predictions.” In this step, you can explore and analyze model predictions on a map from your Amazon SageMaker Studio notebook
How it works
The diagram has six sections. The first section has an image representing data and is titled, “Geospatial ML with Amazon SageMaker.” In this step, you can build, train, and deploy ML models using geospatial data.
The second section has four small images representing data sources and is titled, “Access data sources.” In this step, you can access geospatial data sources, from satellite imagery to mapping and location data, or upload your proprietary data.
The third section has an image of a globe with a small gear overlaying it and is titled, “Transform and enrich data.” In this step, you can use built-in operations such as reverse geocoding, mosaicking, and data labeling to prepare your data.
The fourth section includes an image representing models, with a small computer arrow clicking on them, and is titled, “Select or train a model.” In this step, you can select state-of-the-art, purpose-built models or bring and train your own model.
The fifth section has an image of a rocket flying out of a box and is titled “Deploy a model.” In this step, you can deploy and host your model to make predictions using real-world data.
The sixth and final section includes an image of a magnifying glass over a map and is titled, “Visualize predictions.” In this step, you can explore and analyze model predictions on a map from your Amazon SageMaker Studio notebook