Amazon SageMaker Examples

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Dev Guide

SDK Guide

Introduction

Introduction to Amazon SageMaker

Get started on SageMaker

Customer Churn Prediction with XGBoost

Prepare data

Amazon SageMaker Data Wrangler
Distributed Data Processing using Apache Spark and SageMaker Processing
Get started with SageMaker Processing

Train and tune models

Hyperparameter Tuning with the SageMaker TensorFlow Container
Train a SKLearn Model using Script Mode

Deploy models

Host a Pretrained Model on SageMaker
Deploying pre-trained PyTorch vision models with Amazon SageMaker Neo
Use SageMaker Batch Transform for PyTorch Batch Inference

Track, monitor, and explain models

Amazon SageMaker Multi-hop Lineage Queries
Amazon SageMaker Model Monitor

Orchestrate workflows

Orchestrate Jobs to Train and Evaluate Models with Amazon SageMaker Pipelines
SageMaker Pipelines Lambda Step

Popular frameworks

Regression with Amazon SageMaker XGBoost algorithm
Hugging Face Sentiment Classification
Iris Training and Prediction with Sagemaker Scikit-learn
MNIST Training with MXNet and Gluon
Train an MNIST model with TensorFlow
Train an MNIST model with PyTorch

SageMaker Studio

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

Introduction to Amazon Algorithms

Introduction to Amazon Algorithms

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
Credit Risk
SageMaker Data Wrangler
SageMaker Algorithms with Pre-Trained Model Examples by Problem Type

Autopilot

Get started with Autopilot
Feature selection
Model explainability
Autopilot Pipelines

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
Debugger
Tuning
Distributed Training
Training Compiler
Script Mode
Bring Your Own Container
Management Features
Heterogeneous Clusters

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
Model Governance
Shadow Testing

Workflows

Get started with SageMaker Pipelines
Pipeline Parameterization
SageMaker Pipeline Multi-Model
Pipeline Compare
Launch AutoML with Pipelines
Processing
Spark
Step Functions
Notebook Jobs

Advanced Functionality

Advanced Functionality
Serverless Inference

Advanced examples

SageMaker Clarify
Science of ML
AWS Marketplace
Amazon Sagemaker Geospatial Service

Community examples

Contributions

Amazon SageMaker Examples

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Processing
Edit on GitHub

Processing

Feature transformation with Amazon SageMaker Processing and Dask
Feature transformation with Amazon SageMaker Processing and SparkML
Amazon SageMaker Processing jobs
Distributed Data Processing using Apache Spark and SageMaker Processing
Run PySpark locally on SageMaker Studio

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