Artificial Intelligence
Today, we’re excited to announce a deep-link integration between Hugging Face and Amazon SageMaker AI. Developers can now go from model discovery to hands-on experimentation in SageMaker Studio with a single selection. Whether you fine-tune a foundation model (FM) from Amazon SageMaker JumpStart or deploy it to an Amazon SageMaker Inference endpoint, you can now land directly inside the relevant SageMaker Studio workflow. Your selected model is pre-loaded, and the environment is fully configured and ready to go.
Previously, getting started on SageMaker Studio after discovering a model on Hugging Face required navigating multiple steps. These included opening Amazon SageMaker AI on the AWS Management Console, creating a domain, configuring AWS Identity and Access Management (IAM) permissions, and sometimes requesting graphics processing unit (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.
“At Arcee, we build open models so developers and enterprises can actually own what they run: inspect the weights, post-train on their own data, and deploy on their own terms. This integration takes that promise the last mile. 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. Open weights you own, running in the cloud you control. That is exactly the combination our customers have been asking for.”
