Get Started
Onboard to the Amazon SageMaker AI role and domain.
Automation and Environments
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.
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.
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.