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Amazon SageMaker Documentation

Bringing together AWS machine learning and analytics capabilities, this next generation of Amazon SageMaker provides an integrated experience for analytics and AI with unified access to all of your data. Collaborate and build faster with Amazon SageMaker Unified Studio using familiar AWS tools for model development, generative AI, big data processing, and SQL analytics, accelerated by Amazon Q Developer, the most capable generative AI assistant for software development. Access all of your data whether it's stored in data lakes, data warehouses, third party, or federated sources with Amazon SageMaker Lakehouse. Get built-in governance with Amazon SageMaker Data and AI Governance to align with your enterprise security needs.

Amazon SageMaker Unified Studio

  1. Amazon SageMaker Unified Studio Administrator Guide

    Learn how administrators manage users and groups and set up resources for Amazon SageMaker Unified Studio.

  2. Amazon SageMaker Unified Studio User Guide

    Learn how developers can use the data and tools provided in Amazon SageMaker Unified Studio.

Amazon SageMaker AI

  1. Get Started

    Onboard to the Amazon SageMaker AI role and domain.

  2. Use Automated ML

    Learn how to automate machine learning from start to finish.

  3. Use Machine Learning Environments

    Learn about machine learning environments that Amazon SageMaker AI offers.

  4. Label Data with a Human-in-the-loop

    Learn how to use a human-in-the-loop to help label data more accurately.

  5. Prepare Data

    Learn how to prepare data for machine learning.

  6. Process Data

    Learn how to process data for machine learning.

  7. Create, Store, and Share Features

    Learn how to create, store, and share extracted data signals (features) for machine learning.

  8. Reserve GPU capacity training plans for training jobs or HyperPod clusters

    Learn how to use SageMaker training plans to reserve GPU capacity for your large-scale AI model training workloads.

  9. Train Models

    Learn how to train your machine learning models.

  10. Deploy Models for Inference

    Learn how to deploy your machine learning models for inference.

  11. Implement MLOps

    Learn how to implement machine learning operations on Amazon SageMaker AI.

  12. Monitor Data and Model Quality

    Learn how to monitor data and model quality.

  13. Use Docker Containers to Build Models

    Learn how to use Docker containers to build your machine learning models.

Amazon Bedrock in Amazon SageMaker Unified Studio

  1. Amazon Bedrock User Guide

    Learn how to use Amazon Bedrock in Amazon SageMaker Unified Studio.

Responsible AI

  1. Detect Bias and Understand Explanations

    Learn how to detect bias and understand explanations in machine learning models.

  2. Use Governance

    Learn how to use governance to document and track model performance.

  3. Configure Security in Amazon SageMaker AI

    Learn how to secure resources in Amazon SageMaker AI.

Reference

  1. API Reference

    Describes all of the API operations for Amazon SageMaker AI in detail.

  2. Amazon SageMaker AI Python SDK 

    Use the Amazon SageMaker AI Python SDK library to train and deploy models using popular deep learning frameworks and algorithms.

  3. AWS SDK for Python (Boto3) 

    Use the AWS SDK for Python (Boto3) to format model data and build applications to build, train, and deploy machine learning models.
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