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Learn how to run predictive AI/ML procedures (train, tune, etc.) using AWS SageMaker. These examples focus on narrow "predictive ML/AI" cases, where models are trained to perform a single function (contrasing with "foundation" model use via AWS Bedrock). These materials are directed towards participants of the 2024 Machine Learning Marathon, and some instructions may pertain only to that group. A more general purpose version of this workshop will be made available in future months. Alpha This lesson is in the alpha phase, which means that it has been taught once and lesson authors are iterating on feedback.

    Learn how to run predictive AI/ML procedures (train, tune, etc.) using AWS SageMaker. These examples focus on narrow "predictive ML/AI" cases, where models are trained to perform a single function (contrasing with "foundation" model use via AWS Bedrock). These materials are directed towards participants of the 2024 Machine Learning Marathon, and some instructions may pertain only to that group. A more general purpose version of this workshop will be made available in future months.
    Intro to AWS SageMaker for Predictive ML/AI
    • Intro to AWS SageMaker for Predictive ML/AI
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    Intro to AWS SageMaker for Predictive ML/AI
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    Summary and Schedule
    1. Overview of Amazon SageMaker
    2. Data Storage: Setting up S3
    3. Notebooks as Controllers
    4. Accessing and Managing Data in S3 with SageMaker Notebooks
    5. Using a GitHub Personal Access Token (PAT) to Push/Pull from a SageMaker Notebook
    6. Training Models in SageMaker: Intro
    7. Training Models in SageMaker: PyTorch Example
    8. Hyperparameter Tuning in SageMaker: Neural Network Example
    9. Resource Management and Monitoring

    • Key Points
    • Instructor Notes
    • Extract All Images

    • Glossary
    • Instances for ML

    See all in one page

    Instructor Notes

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    Overview of Amazon SageMaker


    Data Storage: Setting up S3


    Notebooks as Controllers


    Accessing and Managing Data in S3 with SageMaker Notebooks


    Using a GitHub Personal Access Token (PAT) to Push/Pull from a SageMaker Notebook


    Training Models in SageMaker: Intro


    Training Models in SageMaker: PyTorch Example


    Hyperparameter Tuning in SageMaker: Neural Network Example


    Resource Management and Monitoring



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