Written by Takuto Higuchi, Product Marketing Manager, Azure AI
This post was co-authored by Richard Tso, Director of Product Marketing, Azure AI
Open-source technologies have had a profound impact on the world of AI and machine learning, enabling developers, data scientists, and organizations to collaborate, innovate, and build better AI solutions. As large AI models like GPT-3.5 and DALL-E become more prevalent, organizations are also exploring ways to leverage existing open-source models and tools without needing to put a tremendous amount of effort into building them from scratch. Microsoft Azure AI is leading this effort by working closely with GitHub and data science communities, and providing organizations with access to a rich set of open-source technologies for building and deploying cutting-edge AI solutions.
At Azure Open Source Day, we highlighted Microsoft’s commitment to open source and how to build intelligent apps faster and with more flexibility using the latest open-source technologies that are available in Azure AI.
Build and operationalize open-source State-of-the-Art models in Azure Machine Learning
Recent advancements in AI propelled the rise of large foundation models that are trained on a vast quantity of data and can be easily adapted to a wide variety of applications across various industries. This emerging trend provides a unique opportunity for enterprises to build and use foundation models in their deep learning workloads.
Today, we’re announcing the upcoming public preview of foundation models in Azure Machine Learning. It provides Azure Machine Learning with native capabilities that enable customers to build and operationalize open-source foundation models at scale. 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. This service will provide you with a comprehensive repository of popular open-source models for multiple tasks like natural language processing, vision, and multi-modality through the Azure Machine Learning built in registry. Users can not only use these pre-trained models for deployment and inferencing directly, but they will also have the ability to fine-tune supported machine learning tasks using their own data and import any other models directly from the open-source repository.
Read the full article