一键从 Hugging Face 进入 Amazon SageMaker Studio

What’s new

This launch introduces three capabilities that shorten the path from a Hugging Face model to a working SageMaker Studio workflow.

Deep links from Hugging Face into SageMaker Studio

When you browse models on Hugging Face, you’ll now see action buttons alongside supported models that map directly to SageMaker Studio workflows:

Customize on SageMaker AI opens the Model Customization page in Studio with the selected model pre-loaded, ready to fine-tune.

Deploy on SageMaker AI opens the Deployment page in Studio with the model pre-configured for endpoint deployment.

Each entry point preserves the context, meaning you don’t need to search for the model again once inside Studio.

Pre-configured permissions

New Studio environments created through this flow come with permissions already configured for the full range of SageMaker AI capabilities, including model customization, training jobs, notebook experimentation, and endpoint deployment. A new managed policy, AmazonSageMakerModelCustomizationCoreAccess , is created and attached for you. It provides permissions for serverless model customization jobs using supervised fine-tuning (SFT), direct preference optimization (DPO), reinforcement learning with verifiable rewards (RLVR), and reinforcement learning from AI feedback (RLAIF), with supported deployment to SageMaker AI or Amazon Bedrock endpoints. This alleviates the need to manually create and configure AWS Identity and Access Management (IAM) roles and policies before you can start experimenting. For existing Studio environments, actionable messages with direct links to documentation guide you through adding these permissions.

GPU quota visibility

When selecting instance types for deployment or training, the Studio UI now surfaces quota availability directly in the instance selection list. You can immediately see which GPU instance types (G5, G6) are available under your account’s current limits. You don’t need to navigate separately to Service Quotas. If you still need to request a limit increase, you’re redirected directly to the Service Quotas page for the respective instance type.

Walkthrough: Deep-linking from Hugging Face to SageMaker Studio

Let’s walk through the experience of customizing or deploying a model starting from Hugging Face.

Step 1: Discover and select

On the Hugging Face model page, click on “Deploy” and select “Amazon SageMaker AI”. If the model is supported, you will see two buttons, “Deploy on SageMaker AI” and “Customize on SageMaker AI”. Then select “Customize on SageMaker AI” for a supported model.

Step 2: Sign in

You’re prompted to sign in to AWS using your existing credentials. If you already have an active console session, this step is skipped automatically. For more information, see Sign in to the AWS Management Console .

Step 3: Land in Studio

You arrive directly on the Model Customization page inside SageMaker Studio with your model pre-selected. Next, configure your fine-tuning parameters such as training data, hyperparameters, and instance type, then submit the customization job.

Alternatively, selecting Deploy on SageMaker AI opens the endpoint deployment page in Studio with the model pre-configured. Select your instance type (quota visibility included), review the settings, and deploy.

Step 4: Test your endpoint

After you deploy your endpoint, test inference directly from Studio’s endpoint testing interface .

Getting started

You can try this experience today:

Browse models on Hugging Face.

Look for the Customize on SageMaker AI or Deploy on SageMaker AI buttons on supported models.

Select and follow the streamlined sign-in flow.

Start building in a fully configured SageMaker Studio environment.

Conclusion

The launch of a one-click Studio landing experience minimizes the friction between discovering a model and experimenting with it. By connecting Hugging Face directly to the SageMaker Studio workflows, developers can stay in their flow. There’s no context switching, no manual environment setup, and no permission troubleshooting.

一键从 Hugging Face 进入 Amazon SageMaker Studio

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