Project Title

amazon-sagemaker-examples — Comprehensive Jupyter Notebooks for Amazon SageMaker ML Model Development

Overview

The amazon-sagemaker-examples project provides a collection of Jupyter notebooks that serve as practical guides for building, training, and deploying machine learning models using Amazon SageMaker. This repository is maintained by the Amazon SageMaker team and focuses on showcasing the breadth of features offered by SageMaker, including the newly introduced SageMaker-Core SDK for an object-oriented interface to SageMaker resources.

Key Features

SageMaker-Core SDK: A new Python SDK for interacting with SageMaker resources.
Resource Chaining: Pass resource objects as parameters to eliminate manual parameter specification.
Full Parity: Achieves full parity with SageMaker APIs while abstracting low-level details.
Usability Improvements: Auto code completion, comprehensive documentation, and type hints.

Use Cases

Machine Learning Practitioners: Full customization of AWS primitives for ML workloads.
Developers: Seeking an intuitive and efficient way to manage SageMaker resources.
Enterprises: Tailoring ML workloads according to specific needs with intelligent defaults.

Advantages

Streamlined Development: Simplifies code management and integration of SageMaker resources.
Enhanced Developer Experience: Improved with auto code completion and type hints.
Comprehensive Documentation: Facilitates faster coding with fewer errors.

Limitations / Considerations

Limited to AWS Environment: Examples are specific to Amazon SageMaker and AWS services.
Community Examples: Additional examples and reference solutions are maintained in a separate community repository.

SageMaker Example Community repository: Contains additional examples and reference solutions beyond the official repository, maintained by AWS community engineers and solution architects.
Hugging Face Transformers: A library of pre-trained models for Natural Language Processing, which can be integrated with SageMaker for deployment.
TensorFlow: An open-source machine learning framework that can be used with SageMaker for training and deploying models.

Basic Information

GitHub: https://github.com/aws/amazon-sagemaker-examples
Stars: 10,744
License: Unknown
Last Commit: 2025-09-24

📊 Project Information

Project Name: amazon-sagemaker-examples
GitHub URL: https://github.com/aws/amazon-sagemaker-examples
Programming Language: Jupyter Notebook
⭐ Stars: 10,744
🍴 Forks: 6,933
📅 Created: 2017-10-23
🔄 Last Updated: 2025-09-24

🏷️ Project Topics

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📚 Documentation

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amazon-sagemaker-examples

Project Description