amazon-sagemaker-examples
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
💡 Why It Matters
The amazon-sagemaker-examples repository provides practical Jupyter notebooks that help ML/AI teams build, train, and deploy machine learning models using Amazon SageMaker. This open source tool for engineering teams addresses the common challenges of model development and deployment, offering clear examples that simplify the learning curve. With a steady growth of 82 stars over 96 days, it indicates a stable community interest, suggesting that it is a production-ready solution for those looking to leverage SageMaker's capabilities. However, it may not be the right choice for teams seeking highly customised workflows or those who require extensive integration with non-AWS services.
🎯 When to Use
This repository is a strong choice for teams looking to quickly implement machine learning models using AWS infrastructure. However, teams should consider alternatives if they need a self-hosted option or if their projects require specific frameworks not covered in the examples.
👥 Team Fit & Use Cases
Data scientists and ML engineers will find this repository particularly useful as it provides ready-to-use examples for various machine learning tasks. It is commonly integrated into products and systems that rely on AWS for scalable machine learning solutions.
🎭 Best For
🏷️ Topics & Ecosystem
📊 Activity
Latest commit: 2026-02-10. Over the past 97 days, this repository gained 82 stars (+0.8% growth). Activity data is based on daily RepoPi snapshots of the GitHub repository.