惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

IT之家
IT之家
博客园_首页
S
SegmentFault 最新的问题
罗磊的独立博客
博客园 - 【当耐特】
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
阮一峰的网络日志
阮一峰的网络日志
D
Docker
雷峰网
雷峰网
Google DeepMind News
Google DeepMind News
博客园 - 司徒正美
V
V2EX
大猫的无限游戏
大猫的无限游戏
V
Visual Studio Blog
腾讯CDC
宝玉的分享
宝玉的分享
酷 壳 – CoolShell
酷 壳 – CoolShell
人人都是产品经理
人人都是产品经理
T
Tailwind CSS Blog
Vercel News
Vercel News
H
Help Net Security
博客园 - Franky
D
DataBreaches.Net
aimingoo的专栏
aimingoo的专栏

Hugging Face - Blog

Waypoint-1.5: Higher-Fidelity Interactive Worlds for Everyday GPUs ALTK‑Evolve: On‑the‑Job Learning for AI Agents Safetensors is Joining the PyTorch Foundation Holo3: Breaking the Computer Use Frontier Any Custom Frontend with Gradio's Backend A New Framework for Evaluating Voice Agents (EVA) Bringing Robotics AI to Embedded Platforms: Dataset Recording, VLA Fine‑Tuning, and On‑Device Optimizations One-Shot Any Web App with Gradio's gr.HTML CUGA on Hugging Face: Democratizing Configurable AI Agents New in llama.cpp: Model Management Building Deep Research: How we Achieved State of the Art OVHcloud on Hugging Face Inference Providers 🔥 20x Faster TRL Fine-tuning with RapidFire AI Building for an Open Future - our new partnership with Google Cloud Aligning to What? Rethinking Agent Generalization in MiniMax M2 Building a Healthcare Robot from Simulation to Deployment with NVIDIA Isaac Sentence Transformers is joining Hugging Face! Unlock the power of images with AI Sheets Supercharge your OCR Pipelines with Open Models Google Cloud C4 Brings a 70% TCO improvement on GPT OSS with Intel and Hugging Face Get your VLM running in 3 simple steps on Intel CPUs Nemotron-Personas-India: Synthesized Data for Sovereign AI Introducing RTEB: A New Standard for Retrieval Evaluation Accelerating Qwen3-8B Agent on Intel® Core™ Ultra with Depth-Pruned Draft Models VibeGame: Exploring Vibe Coding Games Nemotron-Personas-Japan: ソブリン AI のための合成データセット Swift Transformers Reaches 1.0 – and Looks to the Future Smol2Operator: Post-Training GUI Agents for Computer Use SyGra: The One-Stop Framework for Building Data for LLMs and SLMs Gaia2 and ARE: Empowering the community to study agents
Hugging Face and AWS partner to make AI more accessible
Jeff Boudier, Philipp Schmid, Julien Simon · 2023-02-21 · via Hugging Face - Blog

Back to Articles

Jeff Boudier's avatar

Philipp Schmid's avatar

Julien Simon's avatar

It’s time to make AI open and accessible to all. That’s the goal of this expanded long-term strategic partnership between Hugging Face and Amazon Web Services (AWS). Together, the two leaders aim to accelerate the availability of next-generation machine learning models by making them more accessible to the machine learning community and helping developers achieve the highest performance at the lowest cost.

A new generation of open, accessible AI

Machine learning is quickly becoming embedded in all applications. As its impact on every sector of the economy comes into focus, it’s more important than ever to ensure every developer can access and assess the latest models. The partnership with AWS paves the way toward this future by making it faster and easier to build, train, and deploy the latest machine learning models in the cloud using purpose-built tools.

There have been significant advances in new Transformer and Diffuser machine learning models that process and generate text, audio, and images. However, most of these popular generative AI models are not publicly available, widening the gap of machine learning capabilities between the largest tech companies and everyone else. To counter this trend, AWS and Hugging Face are partnering to contribute next-generation models to the global AI community and democratize machine learning. Through the strategic partnership, Hugging Face will leverage AWS as a preferred cloud provider so developers in Hugging Face’s community can access AWS’s state-of-the-art tools (e.g., Amazon SageMaker, AWS Trainium, AWS Inferentia) to train, fine-tune, and deploy models on AWS. This will allow developers to further optimize the performance of their models for their specific use cases while lowering costs. Hugging Face will apply the latest in innovative research findings using Amazon SageMaker to build next-generation AI models. Together, Hugging Face and AWS are bridging the gap so the global AI community can benefit from the latest advancements in machine learning to accelerate the creation of generative AI applications.

“The future of AI is here, but it’s not evenly distributed,” said Clement Delangue, CEO of Hugging Face. “Accessibility and transparency are the keys to sharing progress and creating tools to use these new capabilities wisely and responsibly. Amazon SageMaker and AWS-designed chips will enable our team and the larger machine learning community to convert the latest research into openly reproducible models that anyone can build on.”

Collaborating to scale AI in the cloud

This expanded strategic partnership enables Hugging Face and AWS to accelerate machine learning adoption using the latest models hosted on Hugging Face with the industry-leading capabilities of Amazon SageMaker. Customers can now easily fine-tune and deploy state-of-the-art Hugging Face models in just a few clicks on Amazon SageMaker and Amazon Elastic Computing Cloud (EC2), taking advantage of purpose-built machine learning accelerators including AWS Trainium and AWS Inferentia.

“Generative AI has the potential to transform entire industries, but its cost and the required expertise puts the technology out of reach for all but a select few companies,” said Adam Selipsky, CEO of AWS. “Hugging Face and AWS are making it easier for customers to access popular machine learning models to create their own generative AI applications with the highest performance and lowest costs. This partnership demonstrates how generative AI companies and AWS can work together to put this innovative technology into the hands of more customers.”

Hugging Face has become the central hub for machine learning, with more than 100,000 free and accessible machine learning models downloaded more than 1 million times daily by researchers, data scientists, and machine learning engineers. AWS is by far the most popular place to run models from the Hugging Face Hub. Since the start of our collaboration, Hugging Face on Amazon SageMaker has grown exponentially. We are experiencing an exciting renaissance with generative AI, and we're just getting started. We look forward to what the future holds for Hugging Face, AWS, and the AI community.