aws/amazon-sagemaker-examples resource

Health v2 · maintenance only

Adoption not part of the score

10980 stars · 6962 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

The official collection of example Jupyter notebooks from the Amazon SageMaker team demonstrating how to build, train, tune, deploy, and monitor machine learning models on AWS SageMaker. It covers the breadth of SageMaker features including Pipelines, Model Monitor, Clarify, and integrations with frameworks like TensorFlow, PyTorch, and Hugging Face.

Use cases

• learn how to train and deploy models on Amazon SageMaker
• example notebooks for SageMaker hyperparameter tuning
• deploy a pretrained Hugging Face model on SageMaker
• set up MLOps pipelines with SageMaker Pipelines
• monitor and explain models with SageMaker Model Monitor and Clarify
• run distributed data processing with Spark on SageMaker
• get started with customer churn prediction using XGBoost

When to choose

• you are using Amazon SageMaker and want official, maintained examples for its features
• you need runnable notebooks covering training, tuning, deployment, and monitoring on AWS
• you want to learn SageMaker-Core, Pipelines, Model Monitor, or Clarify through hands-on examples

When to avoid

• you are not using AWS or SageMaker
• you need production-ready application code rather than tutorial notebooks
• you want community or third-party reference solutions beyond official examples

Facets

learning-resource · maturity active

machine-learning llm-training rag data-science monitoring machine-learning data-science cloud-computing developer-tools tutorials python cloud jvm-scripting jupyter-notebooks aws sagemaker mlops examples model-deployment hyperparameter-tuning

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markdown · JSON · MCP: product_card(name="aws/amazon-sagemaker-examples")