#2626th of 41 in Learning and cookbooks
amazon-sagemaker-examples
Official notebooks for training, tuning, deploying and operating models on SageMaker
- Stars
- 11k
- License
- Apache-2.0
- Last commit
- Oct 2026
- Last release
- Sep 2026
- Language
- Jupyter Notebook
10,816 on 31 Mar 202610,993 on 20 Oct 2026
From GitHub's daily star counts.
Checked on GitHub.
Overview
AWS-maintained collection of Jupyter notebooks covering Amazon SageMaker features: training with ModelTrainer (SDK v3), fine-tuning (SFT, DPO, RLVR, RLAIF, CPT), evaluation, inference endpoints via ModelBuilder, and MLOps with Pipelines and Model Registry. Notebooks are self-contained and load automatically into SageMaker Notebook Instances. They can run elsewhere with a changed IAM role and installed libraries.
Who it is for: ML engineers building and deploying models on AWS SageMaker
Strengths
- Maintained by the SageMaker team, covering training, customization, evaluation, inference and MLOps
- Each notebook is self-contained with its own scripts, data and relative paths
- Covers fine-tuning methods: SFT, DPO, RLVR, RLAIF, MTRL and CPT
- Preloaded in SageMaker Notebook Instances under the SageMaker Examples tab
Weaknesses
- Requires an AWS account, IAM role setup and an S3 bucket
- Tied to AWS services; running elsewhere needs modification
- Only accepts examples for features not already covered; others go to a community repo
- Running examples incurs AWS charges; README gives no cost or instance sizing
What it needs
- Needs AWS account, IAM role, Amazon S3, Amazon SageMaker, Jupyter
- Models: JumpStart foundation models, custom containers, SageMaker Python SDK v3
Also in Learning and cookbooks
See all 41| Rank | Project | Score |
|---|---|---|
| 1 | ai-engineering-from-scratchHands-on AI engineering curriculum with 542 lessons across 20 phases | 89 out of 100 |
| 2 | generative-ai-for-beginners21-lesson course on building generative AI apps in Python and TypeScript | 81 out of 100 |
| 3 | ai-agents-for-beginners18-lesson course on building AI agents with Microsoft Agent Framework | 78 out of 100 |
| #4 | llms-from-scratchCode for the book on building a GPT-style LLM in PyTorch | 77 out of 100 |
| #5 | ml-for-beginners12-week, 26-lesson classic machine learning course using Scikit-learn | 76 out of 100 |