In this video interview, Hugging Face’s Taylor Linton explains how enterprises can use existing open source software instead of building AI applications from scratch.

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Transcript:

Taylor Linton: Predating me joining Hugging Face. We started in 2016 to be a chatbot company. That’s where the fun hugging face name and emoji comes from, because it’s supposed to be a chatbot that’s your friend. So we wanted a friendly brand associated with it. But since I joined Hugging Face in 2021, we started out with 15,000 pre-trained models on the hub. And it was so interesting talking to customers then because it was, Hey, there’s too many models out there, I don’t even know which one to start with. And just seeing that grow from 15,000 to 650,000 in a few years, it really shows how much ai, especially open source has grown.

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It gets even more challenging. So since yesterday when you and I both looked at the models, there’s already 2000 more models. It’s crazy. And the tough part is you can’t really automate these decisions on which model to use. It really is context dependent. You need to know what your cost requirements are, what type of input data that you’re going to be sending to the model, because models are trained on different types of data and you need to try to back into the model that was exposed to your type of domain. During the training. It comes down to latency, the particular task. So these are all things that our engineers can sit down with customers and look at what are we trying to build here? What type of constraints are we working with? And we really walk through them, walk through with them the different trade-offs that you make when you’re picking a different model.

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A lot of CEOs weren’t even aware of what AI was a couple years ago, and then chat GPT came out and CEO’s kids were doing their homework with it and all of a sudden they started hearing about it. So there’s been a lot of interest in companies to try to take advantage of this technology. When folks talk to hugging Face, it’s because we’re the entire open source ecosystem around ai. And so when they want to explore and take advantage of these open source models, they might go to our hub to go grab one of the 650,000 open source models. But also, once they do grab that model and bring it into their environment, we have quite a bit of maybe 20 different libraries or tools that they use to actually go all the way from building to deploying these models. So that’s really when we get inv