Amazon Web Services is betting big on open-source artificial intelligence specialist Hugging Face to make generative AI more accessible and easier to deploy by forming a new collaboration agreement to boost its AI capabilities to provide similar technology that rivals Microsoft-backed ChatGPT.
AWS and Hugging Face have unveiled an expanded partnership that will accelerate the training, fine-tuning and deployment of large language and vision models used to create generative AI applications.
Hugging Face's transformers and natural language processing models are being made available to AWS customers, making it simpler for them to create and deploy AI applications.
"We're expanding our collaboration with Hugging Face to make it easier for developers to access AWS services and perform a variety of tasks including text summarization, answering questions, code generation, image creation, and writing essays and articles," said AWS vice president of database, analytics and machine learning, Swami Sivasubramanian in a LinkedIn post.
Customers can deploy Hugging Face models on top products such as Amazon SageMaker, AWS Trainium and AWS Inferentia.
AWS CEO: Generative AI can ‘transform entire industries'
AWS boss Adam Selipsky said the partnership with Hugging Face will make it easier for customers to access popular machine learning models to create their own generative AI applications with the highest performance and lowest costs.
"Generative AI has the potential to transform entire industries, but its cost and the required expertise put the technology out of reach for all but a select few companies," said Selipsky in a blog post.
"This partnership demonstrates how generative AI companies and AWS can work together to put this innovative technology into the hands of more customers."
Both companies agree the ultimate goal is to make AI open and accessible to all.
Hugging Face has more than 100,000 free and accessible machine learning models that are downloaded more than 1 million times every day. The company is using AWS as its preferred cloud provider so developers can access AWS tools to deploy AI models on AWS.
"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," said Hugging Face CEO Clement Delangue in a blog post.
Daniel Holm, director of enterprise solutions at Intervision System, said the three cloud market-share leaders—Google, Microsoft and AWS—all see massive opportunity in winning customer mindshare with generative AI.
"They're all investing in it for a reason. People are tuned into it now and want to leverage it however they can help their business and employees," said Holm, adding that OpenAI's ChatGPT sparked general public interest in generative AI.
Microsoft-Backed ChatGPT: Google Barb
Microsoft initially invested $1bn into AI start-up OpenAI in 2019, followed by billions of more investment dollars injected into OpenAI over the years.
OpenAI launched ChatGPT to the public in November and has been making headlines ever since due to its AI abilities to do everything from writing essays to creating code. ChatGPT has quickly turned into a go-to tool for many organisations.
This month, Google unveiled a competing generative AI product, dubbed Bard, which will soon be widely available to the public. Bard utilizes Google's Language Model for Dialogue Applications (LaMDA) technology.
AWS and Hugging Face offerings will be available for their thousands of customers and developers. Although not available to the general public, the companies are working together to eventually make their innovation available to the global AI community.
"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," said several Hugging Face executives in a blog post. "To counter this trend, AWS and Hugging Face are partnering to contribute next-generation models to the global AI community and democratize machine learning."
