Run data preprocessing, feature engineering, model evaluation tasks using SageMaker AI processing jobs and built-in or custom containers on fully-managed ML infrastructure.

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

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

Learn about using Amazon SageMaker HyperPod in Amazon SageMaker Studio.

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.

Describes the step types in Amazon SageMaker Pipelines.

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

Describes the machine learning environments supported by Amazon SageMaker AI.

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

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