amazon-sagemaker-examples
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
Official AWS collection of Jupyter notebook tutorials for Amazon SageMaker, covering model training, deployment, data labeling, and machine learning workflows on AWS cloud infrastructure.
This is the official AWS repository of example notebooks for Amazon SageMaker, Amazon's cloud-based machine learning platform. The notebooks use the Jupyter format, which presents code alongside explanations and output in a single interactive document, making them a practical way to follow along with specific tasks rather than just reading documentation.
SageMaker handles the heavy infrastructure side of machine learning: spinning up computing resources for training models, managing where data is stored, and hosting trained models so they can respond to requests. These examples walk through how to use those capabilities for a wide range of tasks, from basic model training to more specialized workflows like geospatial analysis, automated data labeling, and deploying models at scale.
The README notes the collection is split into two repositories. This one is the official set maintained directly by the SageMaker team and focuses on breadth across SageMaker features. A companion community repository exists for additional examples and reference solutions contributed by AWS engineers and architects outside the core team. New pull requests to this official repository are only accepted for features not yet covered anywhere in the existing notebooks.
Getting started requires an AWS account, appropriate permissions set up through AWS Identity and Access Management, an S3 storage bucket for data, and a SageMaker Notebook Instance. Once set up, these notebooks are available directly within the SageMaker interface and can also be run with minimal changes outside of SageMaker by updating the permissions configuration and installing the required Python libraries.
The full README is longer than what was shown.
Where it fits
- Learn how to train a machine learning model on SageMaker by following a working, runnable notebook.
- Deploy a trained model as an API endpoint on AWS using SageMaker Hosting services.
- Set up automated data labeling for a computer vision or NLP dataset using SageMaker Ground Truth.
- Run geospatial machine learning analysis using SageMaker's built-in geospatial processing capabilities.