Learn about AWS managed policies for SageMaker Projects and JumpStart, and recent changes to those policies.

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

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

Sign up for an AWS account, create an admin user, and set up AWS CLI for administrative tasks.

Learn about AWS managed policies for Model Registry and recent changes to those policies.

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

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

Describes the machine learning environments supported by Amazon SageMaker AI.

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.

With the Amazon SageMaker Model Registry you can catalog models for production, manage model versions, associate metadata, and manage the approval status of a model