sagemaker-python-sdk and sagemaker-spark
These are ecosystem siblings serving different integration points—the Python SDK provides direct SageMaker access for ML workflows, while the Spark library enables SageMaker integration within distributed Spark data processing pipelines.
About sagemaker-python-sdk
aws/sagemaker-python-sdk
A library for training and deploying machine learning models on Amazon SageMaker
Provides unified APIs for training with PyTorch, MXNet, and Amazon's optimized algorithms, plus support for custom Docker containers. Version 3.x introduces a modular architecture with separate packages for training (`sagemaker-train`) and inference (`sagemaker-serve`), alongside simplified object-oriented interfaces like `ModelTrainer` and `ModelBuilder` that reduce framework-specific boilerplate. Integrates with SageMaker's distributed training, hyperparameter tuning, JumpStart pre-built models, and local development modes for testing before cloud deployment.
About sagemaker-spark
aws/sagemaker-spark
A Spark library for Amazon SageMaker.
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