Amazon SageMaker Examples
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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
  • Fairness and Explainability with SageMaker Clarify

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
    • Get started with model compilation with Neo
      • Customer Churn Prediction with XGBoost
      • Model Optimization with an Image Classification Example
    • Apache MXNet
      • MNIST Training, Compilation and Deployment with MXNet Module and Sagemaker Neo
      • Deploy pre-trained GluonCV SSD Mobilenet model with SageMaker Neo
    • PyTorch
      • Deploying pre-trained PyTorch vision models with Amazon SageMaker Neo
    • Inf1 Instance
      • Deploying pre-trained PyTorch vision models with Amazon SageMaker Neo On Inf1 Instance
      • Compile Model with Default Settings
      • Compile Model for Multiple Cores using Compiler Options
      • Compiling HuggingFace models for AWS Inferentia with SageMaker Neo
      • Conclusions
      • Compile and Deploy a MXNet model on Inf1 instances
      • Construct a script for training and hosting
      • Deploy the trained model on Inf1 instance for real-time inferences
    • TensorFlow
      • TensorFlow BYOM: Train with Custom Training Script, Compile with Neo, and Deploy on SageMaker
      • Using SageMaker Neo to Compile a Tensorflow U-Net Model
  • 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
  • »
  • Deploy Models with SageMaker »
  • Get started with model compilation with Neo
  • Edit on GitHub

Get started with model compilation with Neo

  • Customer Churn Prediction with XGBoost
  • Model Optimization with an Image Classification Example

Apache MXNet

  • MNIST Training, Compilation and Deployment with MXNet Module and Sagemaker Neo
  • Deploy pre-trained GluonCV SSD Mobilenet model with SageMaker Neo

PyTorch

  • Deploying pre-trained PyTorch vision models with Amazon SageMaker Neo

Inf1 Instance

  • Deploying pre-trained PyTorch vision models with Amazon SageMaker Neo On Inf1 Instance
  • Compile Model with Default Settings
  • Compile Model for Multiple Cores using Compiler Options
  • Compiling HuggingFace models for AWS Inferentia with SageMaker Neo
  • Conclusions
  • Compile and Deploy a MXNet model on Inf1 instances
  • Construct a script for training and hosting
  • Deploy the trained model on Inf1 instance for real-time inferences

TensorFlow

  • TensorFlow BYOM: Train with Custom Training Script, Compile with Neo, and Deploy on SageMaker
  • Using SageMaker Neo to Compile a Tensorflow U-Net Model
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