Amazon SageMaker AI Documentation

Amazon SageMaker AI is a fully managed machine learning service. With Amazon SageMaker AI, data scientists and developers can quickly build and train machine learning models, and then deploy them into a production-ready hosted environment.

Automation and Environments

Get Started

Onboard to the Amazon SageMaker AI role and domain.

Use Automated ML

Learn how to automate machine learning from start to finish.

Use Machine Learning Environments

Learn about machine learning environments that Amazon SageMaker AI offers.

Work with Data

Label Data with a Human-in-the-loop

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

Prepare Data

Learn how to prepare data for machine learning.

Process Data

Learn how to process data for machine learning.

Create, Store, and Share Features

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

Work with Models

Train Models

Learn how to train your machine learning models.

Deploy Models for Inference

Learn how to deploy your machine learning models for inference.

Implement MLOps

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

Monitor Data and Model Quality

Learn how to monitor data and model quality.

Use Docker Containers to Build Models

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

Responsible AI

Detect Bias and Understand Explanations

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

Use Governance

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

Configure Security in Amazon SageMaker AI

Learn how to secure resources in Amazon SageMaker AI.

Reference

API Reference

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

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