Amazon · Hugging Face
Alternatively, selecting Deploy on SageMaker AI opens the endpoint deployment page in Studio with the model pre-configured
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Previously, getting started on SageMaker Studio after discovering a model on Hugging Face required navigating multiple steps between opening Amazon SageMaker AI in the AWS Console, creating a domain, configuring IAM permissions, and sometimes requesting GPU quota.
Key facts
- This alleviates the need to manually create and configure AWS Identity and Access Management (IAM) roles and policies before you can start experimenting
- Previously, getting started on SageMaker Studio after discovering a model on Hugging Face required navigating multiple steps between opening Amazon SageMaker AI in the AWS Console, creating a domain
- It provides permissions for serverless model customization jobs using supervised fine-tuning (SFT), direct preference optimization (DPO), reinforcement learning with verifiable rewards (RLVR)
- With the launch of a one-click Studio landing experience, choosing Customize on SageMaker AI or Deploy on SageMaker AI on a supported Hugging Face model page takes you directly to the console
Summary
“At Arcee, they build open models so developers and enterprises can own what they run: inspect the weights, post-train on their own data, and deploy on their own terms. Going from an open model on Hugging Face straight into SageMaker Studio in a single click, then fine-tuning or deploying it inside your own AWS environment with nothing to wire up, is the kind of experience open models have been missing. — Mark McQuade, Founder and CEO, Arcee AI. With the launch of a one-click Studio landing experience, choosing Customize on SageMaker AI or Deploy on SageMaker AI on a supported Hugging Face model page takes you directly to the console.