5Hugging Face Blog·
Hugging Face models available on Microsoft Foundry Managed Compute
Hugging Face and Microsoft have partnered to make Hugging Face models deployable via Azure AI Foundry's Managed Compute infrastructure. The integration allows developers to deploy open-weight models from the Hugging Face Hub directly through Azure's managed serving layer. This deepens the existing relationship between the two companies and expands enterprise access to open-weights models on Azure.
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Hugging Face Model Catalog Launches on Azure via Microsoft Collaboration
Hugging Face and Microsoft have partnered to make Hugging Face models available through a dedicated Model Catalog on Azure. This integration allows enterprise users to deploy Hugging Face models directly within Azure infrastructure. The collaboration represents a significant distribution channel expansion for open-weight and hosted models into Microsoft's cloud ecosystem.
Microsoft and Hugging Face Expand Collaboration on Azure AI Foundry
Microsoft and Hugging Face are deepening their partnership, with Hugging Face models and tools becoming more tightly integrated into Azure AI Foundry. This expansion likely covers model hosting, fine-tuning, and deployment capabilities within Microsoft's enterprise AI platform. The collaboration positions Azure AI Foundry as a key destination for open-weight model deployment at scale.
Hugging Face and Microsoft Deepen Collaboration: Cloud to Developers
Hugging Face and Microsoft announced an expanded collaboration integrating Hugging Face's model hub and tools more deeply into Microsoft Azure and developer workflows. The partnership extends existing cloud integrations to make open-weight models and ML tooling more accessible via Azure infrastructure. This represents a continued strategic alignment between the leading open-source ML platform and Microsoft's cloud ecosystem.
Hugging Face Models Now Available in Amazon Bedrock Marketplace
Hugging Face has announced that its models are now accessible through Amazon Bedrock's model marketplace, enabling AWS customers to deploy Hugging Face models via Bedrock's managed infrastructure. This integration allows enterprise users to access open-weight and proprietary Hugging Face models without managing their own inference infrastructure. The partnership expands the distribution channel for Hugging Face models into AWS's enterprise customer base.
Hugging Face and AWS Partner to Make AI More Accessible
Hugging Face announced a strategic partnership with Amazon Web Services to expand access to AI models and tools. The collaboration aims to integrate Hugging Face's model hub and libraries more deeply with AWS infrastructure and services. This represents a significant enterprise deployment and cloud distribution move for the open-source AI ecosystem.
Deploy Hugging Face Models Easily with Amazon SageMaker
Hugging Face and Amazon SageMaker announced an integration enabling streamlined deployment of Hugging Face models via SageMaker's managed infrastructure. The partnership provides dedicated Hugging Face Deep Learning Containers on AWS, simplifying the path from model hub to production inference. This represents an early milestone in the enterprise deployment pattern of hosted model hubs integrating with cloud ML platforms.
Hugging Face and Google Partner for Open AI Collaboration
Hugging Face and Google have announced a partnership focused on open AI collaboration, expanding access to Hugging Face models and tools on Google Cloud Platform. The deal deepens integration between Hugging Face's model hub and Google's cloud infrastructure, enabling easier deployment of open-source models via GCP services. This follows a pattern of major cloud providers forming strategic alliances with leading open-source AI platforms.
Accelerating over 130,000 Hugging Face Models with ONNX Runtime
Hugging Face and Microsoft have integrated ONNX Runtime (ORT) to accelerate inference for over 130,000 models on the Hugging Face Hub. The integration enables optimized deployment across CPU and GPU hardware without requiring users to manually export or configure ONNX models. This represents a significant expansion of ORT's reach within the open-weights model ecosystem, lowering the barrier to production-grade inference optimization.