Microsoft and open source natural language processing (NLP) platform Hugging Face has been in partnership since last year. That initial collaboration focused on building Hugging Face Endpoints – a machine learning inference service that is underpinned by Azure ML Managed Endpoint. Now Microsoft says that Azure Machine Learning now has Hugging Face foundation models.
Speaking at its Azure Open Source Day, Microsoft confirmed the new collaboration, which brings a public preview of foundation models to Azure Machine Learning. Customers of the platform can now build their own open-source foundation models and then scale them.
“With these new capabilities, organizations will get access to curated environments and Azure AI Infrastructure without having to manually manage and optimize dependencies. Azure Machine learning professionals can easily start their data science tasks to fine-tune and deploy foundation models from multiple open-source repositories, starting from Hugging Face, using Azure Machine Learning components and pipelines.”
Users can leverage foundation models from various open source repositories (Hugging Face is just one) to help improve their foundation models and then deploy them. Capabilities include being able to use a pre-trained model for inference and deployment, as well as machine learning-supported tasks from customer data.
Hugging Face Partnerships
Microsoft’s partnership with Hugging Face started last year when a new Machine Learning interface that is operated by Microsoft’s ML Managed Endpoints on Azure. Known as Hugging Face Endpoints, the interface allows developers to build and deploy tens of thousands of Transformer models efficiently.
Hugging Face is a multi-vendor company and last month announced a strategic partnership with Amazon Web Services (AWS). The partnership aims to make it easier for developers and companies to leverage machine learning models and ship NLP features faster. The Hugging Face community can now access AWS’s machine learning services and infrastructure.
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