# aws/amazon-sagemaker-examples

Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.

Repository: https://github.com/aws/amazon-sagemaker-examples
Canonical: https://ross.abutalabs.com/products/amazon-sagemaker-examples
Homepage: https://sagemaker-examples.readthedocs.io
Language: Jupyter Notebook
License: Apache-2.0
License Family: permissive
Topics: sagemaker, aws, reinforcement-learning, machine-learning, deep-learning, examples, jupyter-notebook, mlops, data-science, training, inference
Last push: 2026-07-30T22:05:30+00:00

## Health v2 (maintenance only)
Score: 66/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 95, release rhythm 8, longevity 100
- inputs: {"age_days": 3236, "days_push": 34, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 10980, forks 6962 (observed 2026-08-28T04:10:44.963400+00:00)

## What it is
The official collection of example Jupyter notebooks from the Amazon SageMaker team demonstrating how to build, train, tune, deploy, and monitor machine learning models on AWS SageMaker. It covers the breadth of SageMaker features including Pipelines, Model Monitor, Clarify, and integrations with frameworks like TensorFlow, PyTorch, and Hugging Face.

## Use cases
- learn how to train and deploy models on Amazon SageMaker
- example notebooks for SageMaker hyperparameter tuning
- deploy a pretrained Hugging Face model on SageMaker
- set up MLOps pipelines with SageMaker Pipelines
- monitor and explain models with SageMaker Model Monitor and Clarify
- run distributed data processing with Spark on SageMaker
- get started with customer churn prediction using XGBoost

## When to choose
- you are using Amazon SageMaker and want official, maintained examples for its features
- you need runnable notebooks covering training, tuning, deployment, and monitoring on AWS
- you want to learn SageMaker-Core, Pipelines, Model Monitor, or Clarify through hands-on examples

## When to avoid
- you are not using AWS or SageMaker
- you need production-ready application code rather than tutorial notebooks
- you want community or third-party reference solutions beyond official examples

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, llm-training, rag, data-science, monitoring
- domain: machine-learning, data-science, cloud-computing, developer-tools, tutorials
- platform: python, cloud, jvm-scripting
- tags: jupyter-notebooks, aws, sagemaker, mlops, examples, model-deployment, hyperparameter-tuning

## Member repositories
- aws/amazon-sagemaker-examples (main) score 66

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:44.963400+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:17:15.379106+00:00, confidence not recorded.
  - readme: https://github.com/aws/amazon-sagemaker-examples (fetched 2026-08-28T04:10:44.963400+00:00, sha 111507eb2556)
  - homepage: https://sagemaker-examples.readthedocs.io (fetched 2026-08-29T08:15:48.949636+00:00, sha 5c120cca2487)
- Data as of 2026-08-30T08:39:29.467469+00:00.