Learn about AWS managed policies for Amazon SageMaker Feature Store and recent changes to those policies.

Define least-privilege user permissions for machine learning personas with Amazon SageMaker Role Manager.

Use automated ML to automate key machine learning tasks with Amazon SageMaker AI.

List of features offered by Amazon SageMaker AI: new features, machine learning environments, and major features.

Learn about AWS managed policies for SageMaker AI and recent changes to those policies.

Learn about Amazon SageMaker Canvas, a service that you can use to get machine learning predictions and build models without using any code.

Learn about Amazon SageMaker AI, including information for first-time users.

Learn how to explain and detect bias with Amazon SageMaker Clarify.

SageMaker HyperPod is a capability of SageMaker AI that provides an always-on machine learning environment on resilient clusters. You can use these clusters to run any machine learning workloads for developing state-of-the-art machine learning models such as large language models (LLMs) and diffusion models.

Learn about Amazon SageMaker Studio, the latest web-based experience for running ML workflows with Amazon SageMaker AI.