Use Amazon SageMaker HyperPod recipes to get started with training and fine-tuning publicly available foundation models.

Learn how to deploy your machine learning models for real-time inference using SageMaker AI hosting services. Includes information about the options available.

Provides links to AWS SDK developer guides and to code example folders (on GitHub) to help interested customers quickly find the information they need to start building applications.

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

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.

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

Describes the step types in Amazon SageMaker Pipelines.

Learn about notebook-based noninteractive workflows.

Describes Amazon SageMaker Studio Lab and how to use it.

Describes Amazon SageMaker Projects.