The Startup Building the GitHub of Machine Learning

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There are so many A.I. companies we haven’t heard of yet that are doing incredible things, that have yet to go IPO. For most of us, we only hear about them when they have gotten bigger.

However as a serious A.I. Newsletter, I want to cover many of them in a methodical way. A new journal on AiSupremacy that lives at the top of my homepage is Prospectus. Prospectus covers new startups, IPOs, Venture capital investments in A.I. companies.

If I write a sponsored post, I will always disclose it. If I do not disclose it, it means it’s purely of my own interest and usually coincides with PR around funding or whatever the case may be. If you scroll through my archives, you will find a fair number of these already. I try to cover what I consider high-quality A.I. startups.

AI Supremacy

Breaking News: Sequoia Leads Series A Funding in the company that Will Lead Enterprise SaaS Prediction

So what the heck is Hugging Face? Artificial intelligence startup Hugging Face has raised $100 million in Series C funding at a valuation of $2 billion, in the latest sign of its rapid ascendance as a GitHub-like code repository for machine learning practitioners.

They have raised $161 million thus far, according to data from Crunchbase. This is their motto:

The AI community building the future.

Build, train and deploy state of the art models powered by the reference open source in machine learning. Their icon is literally an emoticon. How cool is that?

Even more interesting is Sequoia’s blog on why they invested in them.

Over the last few decades, humans have largely mastered the work of analyzing data: transaction records, click streams, anything in a structured format. But we are a storytelling species; most information is language, not data—and for most of history, machines haven’t been able to process our words.

TechCrunch reminds us that Lux Capital is leading the round, with Sequoia and Coatue investing in the company for the first time. Some of the startup’s existing investors participated once again. These investors include Addition, Betaworks, AIX Ventures, Cygni Capital, Kevin Durant and Olivier Pomel.

Read Hugging Face on Series-C

Addition, a_capital, SV Angel, Betaworks, AIX Ventures, Kevin Durant, Rich Kleiman from Thirty Five Ventures, Olivier Pomel (co-founder & CEO at Datadog) and more. The intersection of Venture Capital and A.I. is really fascinating to me.

Transformers are Hugging your Face

Hugging Face released the Transformers library on GitHub and instantly attracted a ton of attention — it currently has 62,000 stars and 14,000 forks on the platform.

Thanks to the breakthrough emergence of pre-trained transformers and the accompanying flood of language models, one of the hardest, most valuable problems in machine learning is beginning to crack.

Text is quickly becoming just as easy to analyze as numbers—and along with it, context and intent. This profound change in the power of software has applications that reach far beyond chatbots and AI assistants, to everything from fraud detection to bias mitigation to categorizing groceries.

At the heart of this decade-defining trend sits Hugging Face, the company that bridges academia and applications and puts ready-to-use, state-of-the art machine learning models in the hands of developers everywhere.

The first to implement Google’s landmark model BERT in the popular ML library Pytorch and share it with the open-source community, Hugging Face now offers a curated collection of more than 50,000 public models and both cloud and on-prem hosting options, allowing users to easily go from “I have data” to “my model is running in production.”

More than Meets the Eye

With Transformers, you can leverage popular NLP models, such as BERT, GPT-2, T5 or DistilBERT and use those models to manipulate text in one way or another. For instance, you can classify text, extract information, automatically answer questions, summarize text, generate text, etc.

I always get a bit carried away when talking about Transformers.

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Building the GitHub of Machine Learning

Due to the success of this libary, Hugging Face quickly became the main repository for all things related to machine learning models — not just natural language processing. On the company’s website, you can browse thousands of pre-trained machine-learning models, participate in the developer community with your own model, download datasets and more.

Essentially, Hugging Face is building the GitHub of machine learning. It’s a community-driven platform with a ton of repositories. Developers can create, discover and collaborate on ML models, datasets and ML apps.

Here really is then a community for A.I. democratization.

Essentially, Hugging Face is building the GitHub of machine learning. It’s a community-driven platform with a ton of repositories. Developers can create, discover and collaborate on ML models, datasets and ML apps.

Clément Delangue, Julien Chaumond, Thomas Wolf and their team have some good momentum in 2022. See their LinkedIn page here.

• Machine learning is becoming the default way to build technology. When you think about your average day, machine learning is everywhere: from your Zoom background, to searching on Google, to ordering an Uber or writing an email with auto-complete --it's all machine learning.

Hugging Face is now the fastest growing community & most used platform for machine learning! With 100,000 pre-trained models & 10,000 datasets hosted on the platform for NLP, computer vision, speech, time-series, biology, reinforcement learning, chemistry and more, the Hugging Face Hub has become the Home of Machine Learning to create, collaborate, and deploy state-of-the-art models.

Good to know guys and well done!

Sequoia sounded pretty bullish on them too when they said:

• Hugging Face has earned itself a privileged strategic position in the ML ecosystem.
• It’s the default destination for developers looking for the latest and greatest ML models—and the place natural-language processing scientists and other researchers, etc.…
• From the Allen Institute to Microsoft, it’s where the top academics in machine learning go to distribute their models into the world.

Hugging Face also offers hosted services, such as the Inference API that lets you use thousands of models via a programming interface, and the ability to “AutoTrain” your model.

Riding the Transformer Wave (with a happy face)

I can also see how this continues to scale. Think about it, Transformers as a technology have started to appear in computer vision, structured data, biological chemistry and other modalities, accelerating the adoption of machine learning more broadly—and as use cases expand, so does Hugging Face.

With an HQ in New York, Hugging Face allows users to build, train, and deploy art models using the reference open source in machine learning.

In 2022, I think they have demonstrated product-market fit and maybe scale as well.

Over 10,000 companies are now using Hugging Face to build technology with machine learning.

In a future where machine learning is becoming the default way to build technology, the successor to the big data revolution may be the “big content” revolution? Hugging Face will meet you there.

Thanks for reading guys!

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