Create a SageMaker inference endpoint with custom model & extended container

This AWS blog post provides a comprehensive guide to creating a custom SageMaker inference endpoint using the NASA Prithvi geospatial AI model. The article demonstrates how to:

• Extend a SageMaker container image with custom dependencies
• Create a custom inference.py file for model initialization and prediction
• Build and deploy a custom model using AWS CodeBuild and SageMaker
• Create a SageMaker endpoint for real-time inference with GPU acceleration

Key technical steps include:

• Extending a PyTorch SageMaker container with MMCV library
• Implementing custom model loading and inference functions
• Packaging model artifacts in a specific S3 file structure
• Creating IAM roles and configuring SageMaker endpoint resources

The solution enables deploying complex custom models with unique dependencies on SageMaker, providing flexibility for specialized machine learning use cases.