ML platform examples
Repository of example Jupyter notebooks demonstrating how to build, train, and deploy machine learning models with Amazon SageMaker.
Solves the same problem with a different architecture or at a different layer. Expect to rewrite the integration.
Choose if you use Azure ML as your managed ML platform instead of SageMaker.
Python notebooks with ML and deep learning examples with Azure Machine Learning Python SDK | Microsoft
Choose if you want a structured ML engineering course with notebooks over platform-specific examples.
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An earlier or more limited way to do the job. Still the right call when you need something small, proven, or CPU-only.
Choose if you target Intel edge inference with OpenVINO rather than SageMaker cloud deployment.
📚 Jupyter notebook tutorials for OpenVINO™
Choose if you need computer-vision-specific examples for Roboflow workflows.
A collection of tutorials on state-of-the-art computer vision models and techniques. Explore everything from foundational architectures like ResNet to cutting-edge models like RF-DETR, YOLO11, SAM 3, and Qwen3-VL.
These projects were analysed and named amazon-sagemaker-examples among their alternatives. The relationship is not symmetric — how amazon-sagemaker-examples rates them is a separate judgement, made when amazon-sagemaker-examples is analysed in its own right.
calls amazon-sagemaker-examples “Vendor-specific notebooks for SageMaker, narrower than a general production ML course.”
Learn how to develop, deploy and iterate on production-grade ML applications.