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Hugging Face and Amazon Web Services (AWS) recently announced a partnership aimed at improving and streamlining AI models to make them more accessible for developers and data scientists. The partnership aims to achieve this by enabling developers and data scientists to more easily access and utilize Hugging Face's powerful machine learning models, and making it simpler to deploy them at scale.

Integration with AWS SageMaker

"Together, we aim to make AI development and deployment accessible to a broader range of users, driving innovation and progress in the field of AI." – Hugging Face

The partnership between Hugging Face and AWS will integrate Hugging Face's AI model repository with AWS SageMaker, a fully managed machine learning service that enables data scientists and developers to build, train, and deploy machine learning models quickly. By integrating with SageMaker, Hugging Face's AI models will be optimized for AWS cloud infrastructure, making it easier for developers to train and deploy them at scale.

In an interview with InfoQ, Hugging Face CEO Clement Delangue explained that the integration with SageMaker will allow Hugging Face's AI models to be more easily incorporated into customer workflows. "We believe this integration will significantly reduce the amount of time and effort needed to deploy models into production," Delangue said.

Offerings through AWS Marketplace, Inferentia, and Trainium

In addition to integrating with SageMaker, Hugging Face's AI models will also be available through AWS Marketplace, a digital catalog with thousands of software listings from independent software vendors. By offering Hugging Face's models through AWS Marketplace, AWS is making it easier for developers to access and use Hugging Face's state-of-the-art models for their own applications.

According to Hugging Face's blog post:

"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"

In an interview with InfoQ, AWS VP of AI Swami Sivasubramanian noted that the partnership with Hugging Face was part of a larger trend toward making AI more accessible for developers and data scientists. "What we're seeing is a proliferation of AI models, and it's becoming increasingly challenging for developers and data scientists to know which ones to use, how to use them, and how to incorporate them into their workflows," Sivasubramanian said. "Our partnership with Hugging Face is aimed at addressing these challenges and making it easier for customers to access and use the best AI models for their applications."

QuickSight Q

AWS is also introducing a new AI service called QuickSight Q that uses Hugging Face's natural language generation models to automatically generate insights and narratives from data. QuickSight Q is a new natural language processing (NLP) feature that enables users to ask questions about their data using plain language. With this feature, QuickSight leverages machine learning algorithms to interpret the user's queries and provide answers in the form of visualizations like graphs, tables, and charts. The tool simplifies the process of analyzing data and makes it more accessible for non-technical users. QuickSight Q eliminates the need for manual data queries, complicated SQL knowledge, or understanding complex data queries to derive insights from data.

According to AWS, QuickSight Q is a game-changer for businesses, especially those that need to make data-driven decisions but face a lack of resources, time, or expertise to do so. The QuickSight Q feature offers an opportunity to democratize data access within organizations by making data analysis more straightforward and intuitive, empowering anyone, regardless of technical knowledge or background, to derive insights from their data. Overall, QuickSight Q has the potential to revolutionize the way businesses interact with data, enabling them to make more informed decisions.

Conclusion

The partnership between Hugging Face and AWS is a significant step forward in making AI models more accessible and easier to use for developers and data scientists. By leveraging the strengths of both companies, the partnership aims to streamline the AI development process, improve model performance and accuracy, and make AI accessible to a broader range of users.

By integrating Hugging Face's model repository with AWS SageMaker, optimizing Hugging Face's AI tools for AWS cloud infrastructure, making AI models more accessible with AWS services, and using Hugging Face's NLG models with QuickSight Q, the partnership offers a comprehensive solution for AI development and deployment on AWS. This will enable developers and data scientists to build, train, and deploy AI models more quickly, efficiently, and accurately, driving innovation and progress in the field of AI.