aws/amazon-sagemaker-examples
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
Covers the full ML lifecycle from data preparation through inference monitoring, with notebooks organized by workflow stage (prepare data, build/train, deploy/monitor). Introduces SageMaker-Core, a new Python SDK offering resource chaining and object-oriented abstractions over low-level SageMaker APIs, alongside traditional Boto3 patterns. Integrates with AWS primitives including managed training jobs, real-time/serverless/asynchronous endpoints, and CloudWatch-based model monitoring for drift detection.
10,883 stars.
Stars
10,883
Forks
6,987
Language
Jupyter Notebook
License
Apache-2.0
Category
Last pushed
Feb 24, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/aws/amazon-sagemaker-examples"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
aws/sagemaker-python-sdk
A library for training and deploying machine learning models on Amazon SageMaker
aws-samples/sagemaker-ssh-helper
A helper library to connect into Amazon SageMaker with AWS Systems Manager and SSH (Secure Shell)
aws/sagemaker-xgboost-container
This is the Docker container based on open source framework XGBoost...
aws-samples/aws-ml-enablement-workshop
組織横断的にチームを組成し、機械学習による成長サイクルを実現する計画を立てるワークショップ
aws-deepracer-community/deepracer-on-the-spot
Repo for running DeepRacer on Spot or Standard instances to save money versus the AWS DeepRacer console