amazon-sagemaker-examples and studio-lab-examples
These are ecosystem siblings—both provide example notebooks for different SageMaker products (full SageMaker vs. the free-tier Studio Lab alternative), sharing the same underlying platform and use cases but targeting different user segments and deployment contexts.
About amazon-sagemaker-examples
aws/amazon-sagemaker-examples
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
Covers the full ML lifecycle from data preparation through inference monitoring, with notebooks organized by workflow stage (prepare data, build/train, deploy/monitor). Introduces SageMaker-Core, a new Python SDK offering resource chaining and object-oriented abstractions over low-level SageMaker APIs, alongside traditional Boto3 patterns. Integrates with AWS primitives including managed training jobs, real-time/serverless/asynchronous endpoints, and CloudWatch-based model monitoring for drift detection.
About studio-lab-examples
aws/studio-lab-examples
Example notebooks for working with SageMaker Studio Lab. Sign up for an account at the link below!
Curated notebooks spanning computer vision, NLP, geospatial analysis, and generative AI with one-click integration into Studio Lab via custom "Open in Studio Lab" buttons. Features pre-configured Conda environments for frameworks like PyTorch, Hugging Face, fast.ai, and AutoGluon, enabling reproducible ML workflows without manual setup. Demonstrates progression from local experimentation to production deployment on Amazon SageMaker endpoints using boto3.
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