Learn about creating and managing Amazon EMR clusters from Amazon SageMaker Studio Classic to process large-scale data for machine learning workloads.

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

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

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

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

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

Learn about using Amazon SageMaker HyperPod in Amazon SageMaker Studio.

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

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