The Partnership: Amazon SageMaker and Hugging Face

The Partnership: Amazon SageMaker and Hugging Face

Hugging Face Blog 17 min read

About this article

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

Back to Articles The Partnership: Amazon SageMaker and Hugging Face Published March 23, 2021 Update on GitHub Upvote 4 Philipp Schmid philschmid Follow Look at these smiles! Today, we announce a strategic partnership between Hugging Face and Amazon to make it easier for companies to leverage State of the Art Machine Learning models, and ship cutting-edge NLP features faster. Through this partnership, Hugging Face is leveraging Amazon Web Services as its Preferred Cloud Provider to deliver services to its customers. As a first step to enable our common customers, Hugging Face and Amazon are introducing new Hugging Face Deep Learning Containers (DLCs) to make it easier than ever to train Hugging Face Transformer models in Amazon SageMaker. To learn how to access and use the new Hugging Face DLCs with the Amazon SageMaker Python SDK, check out the guides and resources below. On July 8th, 2021 we extended the Amazon SageMaker integration to add easy deployment and inference of Transformers models. If you want to learn how you can deploy Hugging Face models easily with Amazon SageMaker take a look at the new blog post and the documentation. Features & Benefits 🔥 One Command is All you Need With the new Hugging Face Deep Learning Containers available in Amazon SageMaker, training cutting-edge Transformers-based NLP models has never been simpler. There are variants specially optimized for TensorFlow and PyTorch, for single-GPU, single-node multi-GPU and multi-node clusters. Accel...

Originally published on February 15, 2026. Curated by AI News.

Related Articles

[2603.25112] Do LLMs Know What They Know? Measuring Metacognitive Efficiency with Signal Detection Theory
Llms

[2603.25112] Do LLMs Know What They Know? Measuring Metacognitive Efficiency with Signal Detection Theory

Abstract page for arXiv paper 2603.25112: Do LLMs Know What They Know? Measuring Metacognitive Efficiency with Signal Detection Theory

arXiv - AI · 4 min ·
[2603.24772] Evaluating Fine-Tuned LLM Model For Medical Transcription With Small Low-Resource Languages Validated Dataset
Llms

[2603.24772] Evaluating Fine-Tuned LLM Model For Medical Transcription With Small Low-Resource Languages Validated Dataset

Abstract page for arXiv paper 2603.24772: Evaluating Fine-Tuned LLM Model For Medical Transcription With Small Low-Resource Languages Val...

arXiv - Machine Learning · 4 min ·
[2603.25325] How Pruning Reshapes Features: Sparse Autoencoder Analysis of Weight-Pruned Language Models
Llms

[2603.25325] How Pruning Reshapes Features: Sparse Autoencoder Analysis of Weight-Pruned Language Models

Abstract page for arXiv paper 2603.25325: How Pruning Reshapes Features: Sparse Autoencoder Analysis of Weight-Pruned Language Models

arXiv - AI · 4 min ·
Liberate your OpenClaw
Open Source Ai

Liberate your OpenClaw

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

Hugging Face Blog · 3 min ·
More in Open Source Ai: This Week Guide Trending

No comments

No comments yet. Be the first to comment!

Stay updated with AI News

Get the latest news, tools, and insights delivered to your inbox.

Daily or weekly digest • Unsubscribe anytime