This article is part of our series that explores thebusiness of artificial intelligence.
Large tech companies like Google, Facebook, and Microsoft have been using transformer models for several years.
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The company recently secured$100 million in Series C at a $2 billion valuation.
The company wants to provide a broad range of machine learning services, including off-the-shelf transformer models.
More recently, transformers have also moved into other areas, such as drug research and computer vision.
One of the main advantages of transformer models is their capability to scale.
However, training and running large transformers is very difficult and costly.
Arecent paper by Facebookshows some of the behind-the-scenes challenges of training very large language models.
Hugging Face provides a large repertoire of pre-trained ML models to ease the burden of deploying transformers.
Developers can directly load transformers from the Hugging Face library and run them on their own servers.
Pre-trained models are great for experimentation and fine-tuning transformers for downstream applications.
So, why did Hugging Face turn to Microsoft?
(Currently in beta, Hugging Face Endpoints is free, and users only pay for Azure infrastructure costs.
The company plans a usage-based pricing model when the product becomes available to the public.)
More importantly, Microsoft has access to a large share of the market that Hugging Face is targeting.
Many companies find it frustrating to sign up and pay for various cloud services.
have billions of users and provide plenty of use cases for transformer models.
Company execs have already hinted at plans to expand their partnership with Microsoft.
But Hugging Faces collaboration with Microsoft wont be without tradeoffs.
And Microsoft can always launch a rival product that will be better, faster, and cheaper.
If a Microsoft acquisition proposal comes down the line, Hugging Face will have to make a tough choice.
This is also a reminder of where the market for large language models and applied machine learning is headed.
Indeed, products like Hugging Face Endpoints will democratize machine learning for developers.
Companies like Hugging Face will have to suffer the consequences.
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