# From Hugging Face to Amazon SageMaker Studio in one click

DevFeed: [From Hugging Face to Amazon SageMaker Studio in one click](<https://devfeed.tech/articles/from-hugging-face-to-amazon-sagemaker-studio-in-one-click-7090.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/amazon/one-click-to-sagemaker-studio>)

Author: Hazim Qudah; Naidile Murali; Jeff Boudier; Simon Pagezy; Enrique Hernández Calabrés

Published: 2026-07-07T21:15:33Z

Content type: release

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [Amazon SageMaker AI](<https://devfeed.tech/topics/amazon-sagemaker-ai.md>), [hugging face](<https://devfeed.tech/topics/hugging-face.md>), [Amazon Web Services](<https://devfeed.tech/topics/aws.md>), [AWS IAM](<https://devfeed.tech/topics/aws-iam.md>), [Deployment](<https://devfeed.tech/topics/deployment.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [enterprise deployment](<https://devfeed.tech/topics/enterprise-deployment.md>), [Serverless](<https://devfeed.tech/topics/serverless.md>)

Tags: [amazon-sagemaker-ai](<https://devfeed.tech/tags/amazon-sagemaker-ai.md>), [aws](<https://devfeed.tech/tags/aws.md>), [cloud](<https://devfeed.tech/tags/cloud.md>), [data](<https://devfeed.tech/tags/data.md>), [deployment](<https://devfeed.tech/tags/deployment.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [iam](<https://devfeed.tech/tags/iam.md>), [serverless](<https://devfeed.tech/tags/serverless.md>)

## AI overview

Hugging Face and Amazon SageMaker AI now provide a one-click path from supported model pages to SageMaker Studio. The integration can create a configured Studio environment and preserve model context for customization, fine-tuning, training, experimentation, and endpoint deployment.

## Source excerpt

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. For developers who want to iterate quickly, this friction slows down the path from inspiration to experimentation. The integration creates a more direct path from discovery to enterprise deployment.