Amazon SageMaker Examples
latest

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
    • Feature transformation with Amazon SageMaker Processing and Dask
    • Feature transformation with Amazon SageMaker Processing and SparkML
    • Amazon SageMaker Processing jobs
    • Data pre-processing and feature engineering
    • Training using the pre-processed data
    • Model Evaluation
    • Running processing jobs with your own dependencies
    • Distributed Data Processing using Apache Spark and SageMaker Processing
  • Spark
  • Step Functions

Advanced examples

  • Science of ML
  • AWS Marketplace

Community examples

  • Contributions
Amazon SageMaker Examples
  • Docs »
  • Processing
  • Edit on GitHub

ProcessingΒΆ

  • Feature transformation with Amazon SageMaker Processing and Dask
  • Feature transformation with Amazon SageMaker Processing and SparkML
  • Amazon SageMaker Processing jobs
  • Data pre-processing and feature engineering
  • Training using the pre-processed data
  • Model Evaluation
  • Running processing jobs with your own dependencies
  • Distributed Data Processing using Apache Spark and SageMaker Processing
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