AWS-Logo_White-Color
  • AutoPilot Workshop
    • How Amazon SageMaker Autopilot works
    • Getting Started
    • Load Autopilot Example
    • Run Autopilot Example
    • Workshop Clean-up
  • ML Training Workshop
    • Getting Started
      • Setup
      • Clone the Repository
    • Training with Built-in Algo
      • Built-in Algorithms
      • House Price Prediction
      • House Price Prediction Experiment
      • Image Classification
    • Training with your own script
      • Training with your own script
      • House Price Prediction
      • House Price Prediction-HPO
      • Img Classification
      • Img Classification-Pipe Mode
      • Img Classification-Experiment
      • Img Classificaiton-Distributed
      • Img Classificaiton-Spot training
      • Img Classification-Debugger
    • Training with your own container
      • Bring Your Own Container
      • House Price Prediction
      • Image Classification
  • SageMaker Operators for Kubernetes Workshop
    • Getting Started
      • Overview
      • Prerequisites
      • Install Eksctl
      • Create Cluster
      • Install Kubeflow
      • Install SageMaker Operators
    • Dashboard
      • Dashboard
    • Notebook
      • Create a notebook
      • Data Preparation
    • Training and Inference
      • Training
      • Inference
      • Batch Inference
      • Hyper Parameter Optimization
    • Improving the TensorFlow Model
      • Network Improvements
      • Data Preparation
      • Training Script
      • Training and Inference
    • Next Steps
      • Next Steps
      • Cleanup
  • SageMaker Components for Kubeflow Pipelines Workshop
    • Getting Started
      • Overview
      • Prerequisites
    • Creating the Pipeline
      • S3 Bucket
      • SageMaker Job Role
      • Compile the Pipeline Definition
      • Register the Pipeline
    • Running the Pipeline
      • Run the Pipeline
      • Check Pipeline Status
      • Batch Transform Output
      • Real Time Inference
    • Deep Learning Pipeline
      • Create a Jupyter Notebook
      • Create and Run Pipeline
    • Next Steps
  • Resources
    • Use Existing Code
      • Use Existing Tensorflow Code
      • Use Existing Pytorch Code
      • Use Existing MxNet Code
      • Use Existing Scikit Learn Code
      • Use Existing R Code
      • Using SageMaker Spark
    • Advanced Topics
      • Reinforcement Learning
      • Graph Neural Networks
      • Automatically Stopping Training jobs
      • Generative AI
      • Iterative Model Pruning
      • Model specific real-time analysis
      • Visualization related
    • Sample SageMaker Use Cases
      • Structured data
      • Time Series
      • Computer Vision
      • Natural Language Processing
      • SageMaker Marketplace Examples on SageMaker
    • Specific SageMaker Features
      • Prepare Datasets
      • Build, Train and Deploy models
      • Use Advanced Training capabilities
      • Use Advanced Deployment capabilities
      • Optimize Costs

More

  • More Resources
  • Authors
Privacy | Site Terms | © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Edit this page
Amazon SageMaker Workshop > Resources > Advanced Topics

Advanced Topics

  • Reinforcement Learning
    1. Reinforcement Learning examples
  • Graph Neural Networks
    1. Deep Graph Library on SageMaker
    2. Graph convolutional Networks
  • Automatically Stopping Training jobs
    1. Tensorflow stop training job based on rule
    2. Stop notebook instance based on idle CPU cycles
    3. Other lifecycle configuration scripts for SageMaker
  • Generative AI
    1. Generative LDA details
    2. Generate new text from Wiki data
  • Iterative Model Pruning
    1. Iterative Model Pruning in Pytorch
  • Model specific real-time analysis
    1. Model specific realtime analysis for Autoencoders
    2. Model specific realtime analysis for BERT attention head view
    3. Model specific realtime analysis for CNNs
  • Visualization related
    1. MNIST Tensor Plot example
    2. MNIST Tensor Analysis example
    3. MXNET Realtime Analysis example