Amazon SageMaker Examples
website

Get started

  • Introduction to applying machine learning
    • Predicting Product Success When Review Data Is Available
    • Breast Cancer Prediction
    • Customer Churn Prediction with XGBoost
  • Training
    • Multiclass classification with Amazon SageMaker XGBoost algorithm
    • Set up hosting for the model
    • Ensemble Predictions From Multiple Models
  • Inference
    • Introduction
    • Creating SageMaker Inference Endpoint

Featured examples

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

Autopilot

  • Get started with Autopilot
  • Feature selection
  • Model explainability

Preprocessing

  • Preprocessing by data type
  • Ground Truth

Training

  • Algorithms
  • Bring your own container
  • Data types
  • Debugger
  • Distributed Training
  • Experiments
  • Frameworks
  • Hyperparameter tuning
  • Management features
  • Reinforcement learning

Inference

  • Batch transform
  • Bring your own container
  • Data types
  • Model Compilation with Neo
  • Model deployment
  • Model monitor

Frameworks

  • Apache MXNet
  • Deep Graph Library
  • PyTorch
  • R
  • Scikit-learn
  • TensorFlow

Workflows

  • Processing
  • Spark
  • Step Functions

Advanced examples

  • Science of ML
  • AWS Marketplace

Community examples

  • Contributions
Amazon SageMaker Examples
  • Docs »
  • Introduction to applying machine learning
  • Edit on GitHub

Introduction to applying machine learning¶

  • Predicting Product Success When Review Data Is Available
  • Breast Cancer Prediction
  • Customer Churn Prediction with XGBoost

Training¶

  • Multiclass classification with Amazon SageMaker XGBoost algorithm
  • Set up hosting for the model
  • Ensemble Predictions From Multiple Models

Inference¶

  • Introduction
  • Creating SageMaker Inference Endpoint
Next Previous

© Copyright 2020, Aaron Markham Revision 28420d84.

Built with Sphinx using a theme provided by Read the Docs.