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  1. aws.amazon.com › sagemakerMachine Learning Service - Amazon SageMaker - AWS

    With SageMaker, you can build, train and deploy ML models at scale using tools like notebooks, debuggers, profilers, pipelines, MLOps, and more – all in one integrated development environment (IDE).

    • Resources

      Documentation Amazon SageMaker helps data scientists and...

    • Data Scientists

      SageMaker Studio offers a unified experience to perform all...

    • Getting Started

      Get started building with Amazon SageMaker in the AWS...

    • Pipelines

      Amazon SageMaker Pipelines is a purpose-built workflow...

    • MLOps

      Amazon SageMaker provides purpose-built tools for machine...

    • Train

      To train deep learning models faster, SageMaker helps you...

    • Canvas

      Amazon SageMaker Canvas supports the full ML lifecycle...

    • Experiments

      ML experiments are performed in diverse environments,...

  2. docs.aws.amazon.com › sagemaker › latestWhat is Amazon SageMaker? - Amazon SageMaker

    Amazon SageMaker lets you build, train, and deploy ML models into a production-ready environment. Learn how to use SageMaker features, pricing, and low-code and no-code options for ML workflows.

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  4. studiolab.sagemaker.awsSageMaker Studio Lab

    Get started in minutes. The Amazon SageMaker Studio Lab is based on the open-source and extensible JupyterLab IDE. Skip the complicated setup and author Jupyter notebooks right in your browser.

  5. aws.amazon.com › campaigns › sagemakerAmazon SageMaker

    Amazon SageMaker is a service that lets you create, train, and deploy machine learning models without managing infrastructure or workflows. It supports various frameworks, algorithms, and integrations, and offers fast and automatic tuning, testing, and monitoring.

  6. docs.aws.amazon.com › sagemakerAmazon SageMaker Documentation

    Learn how to use Amazon SageMaker, a fully managed machine learning service, to build, train, and deploy models. Find guides, tutorials, API references, and more for data preparation, automation, MLOps, responsible AI, and security.

  7. aws.amazon.com › sagemaker › getting-startedGetting started with Amazon SageMaker - aws.amazon.com

    Learn how to use Amazon SageMaker to accomplish various machine learning lifecycle tasks, such as data preparation, training, deployment, and MLOps. Follow along the hands-on tutorials and watch the demos for different roles and scenarios.

  8. docs.aws.amazon.com › sagemaker › latestAmazon SageMaker Features - Amazon SageMaker

    Learn about the features of Amazon SageMaker, a fully managed service that enables you to build, train, and deploy machine learning models at scale. Explore the new features for re:Invent 2023, such as SageMaker Canvas chat, Code Editor, HyperPod, and more.