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 Pipelines and recent changes to those policies.

Describes the machine learning environments supported by Amazon SageMaker AI.

Describes Amazon SageMaker Studio Lab and how to use it.

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.

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.

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