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

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.

Learn about AWS managed policies for Model Registry 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.

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

Use a batch transform job to get inferences for an entire dataset, when you don't need a persistent endpoint, or to preprocess datasets to remove noise or bias.

Learn about using Amazon SageMaker HyperPod in Amazon SageMaker Studio.

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