Amazon SageMaker Example Notebooks

Welcome to Amazon SageMaker. This site highlights example Jupyter notebooks for a variety of machine learning use cases that you can run in SageMaker.

This site is based on the SageMaker Examples repository on GitHub. To run these notebooks, you will need a SageMaker Notebook Instance or SageMaker Studio. Refer to the SageMaker developer guide’s Get Started page to get one of these set up.

On a Notebook Instance, the examples are pre-installed and available from the examples menu item in JupyterLab. On SageMaker Studio, you will need to open a terminal, go to your home folder, then clone the repo with the following:

git clone https://github.com/aws/amazon-sagemaker-examples.git

• Introduction to Amazon SageMaker

SageMaker Studio

• Get Started with SageMaker Studio
• Framework examples
• Model compilation with Neo
• Bring your own container to Studio

SageMaker End-to-End Examples

• Fraud Detection System
• Music Recommender
• Understanding Trends in Company Valuation with NLP

Patterns

• The ML Gateway Pattern

SageMaker Use Cases

• Music Streaming Service: Customer Churn Detection
• Fleet Predictive Maintenance
• E-Commerce Personalization
• Computer Vision for Medical Imaging
• Pipelines with NLP for Product Rating Prediction

Autopilot

• Get started with Autopilot
• Feature selection
• Model explainability

Ingest Data

• Get started with data ingestion
• Athena
• EMR
• Redshift
• Amazon Keyspaces (for Apache Cassandra)

Label Data

• Ground Truth

Prep Data

• Get started with data prep
• Detect pre-training data bias
• Image data guide
• Tabular data guide
• Text data guide

Feature Store

• Get started with SageMaker Feature Store

Frameworks

• Frameworks and Libraries
• Apache MXNet
• Deep Graph Library
• PyTorch
• R
• Scikit-learn
• TensorFlow
• JAX

Training

• Algorithms
• Reinforcement Learning
• Experiments
• Debugger
• Tuning
• Distributed Training
• Training Compiler
• Script Mode
• Bring Your Own Container
• Management Features

Inference

• Deploy Models with SageMaker
• Batch transform
• Bring your own container
• Data types
• Model Compilation with Neo
• Model deployment
• Model monitor
• Multi-Model Deployment
• Nvidia Triton Inference

Workflows

• Get started with SageMaker Pipelines
• Processing
• Spark
• Step Functions

Advanced examples

• SageMaker Clarify
• Science of ML
• AWS Marketplace

Community examples

• Contributions