Amazon SageMaker Examples
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Get started

  • Introduction to applying machine learning
  • Training
  • Inference

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
    • AWS Step Functions Data Science SDK - Hello World
    • Automate Model Retraining & Deployment Using the AWS Step Functions Data Science SDK
    • Build a machine learning workflow using Step Functions and SageMaker
    • Build machine learning workflows with Amazon SageMaker Processing and AWS Step Functions Data Science SDK
    • MNIST Training using PyTorch and Step Functions

Advanced examples

  • Science of ML
  • AWS Marketplace

Community examples

  • Contributions
Amazon SageMaker Examples
  • Docs »
  • Step Functions
  • Edit on GitHub

Step FunctionsΒΆ

  • AWS Step Functions Data Science SDK - Hello World
  • Automate Model Retraining & Deployment Using the AWS Step Functions Data Science SDK
  • Build a machine learning workflow using Step Functions and SageMaker
  • Build machine learning workflows with Amazon SageMaker Processing and AWS Step Functions Data Science SDK
  • MNIST Training using PyTorch and Step Functions
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