Deploying on AWS documentation
Choose a service
Choose a service
Pick the service that matches how you want to work. Every guide is self-contained and reproducible.
Full control from Python: deploy any Hub model to a managed endpoint, or run custom training jobs on managed infrastructure.
Quickstart →A curated model catalog with performant defaults, deployable in a few clicks from SageMaker Studio.
Quickstart →JumpStart models behind the managed Bedrock APIs: Agents, Knowledge Bases, Guardrails, and Model Evaluations.
Quickstart →Run the Deep Learning Containers directly on AWS compute services when you need control over networking and orchestration.
Quickstart →Hugging Face manages the infrastructure for you, optimized for cost and throughput.
Guide →All offerings run the same Hugging Face Deep Learning Containers. For end-to-end recipes (TRL fine-tuning, embedding models, Inferentia2, and more), see the Examples under SageMaker SDK in the sidebar.
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