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

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

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

Learn about Amazon SageMaker Studio, the latest web-based experience for running ML workflows with Amazon SageMaker AI.

Describes Amazon SageMaker Studio Lab and how to use it.

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

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.

Describes the step types in Amazon SageMaker Pipelines.

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 how to explain and detect bias with Amazon SageMaker Clarify.