Learn about Amazon SageMaker Partner AI Apps, the types of applications supported, how they work, and integration with AWS services.

Learn about the recommendations for a first-time user of Amazon SageMaker AI.

Compile deep learning (DL) models to achieve an improved training efficiency by using Amazon SageMaker Training Compiler.

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.

Describes Amazon SageMaker Studio Lab and how to use it.

Deploy and scale ML models without configuring or managing any of the underlying infrastructure with Amazon SageMaker Serverless Inference.

Learn about notebook-based noninteractive workflows.

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 Amazon SageMaker Projects.