Hugging Face is done being just a model hub. The open-source company now wants to become the world’s largest machine learning classroom, setting a target of teaching ML to 5 million people by the end of 2023. That’s a massive jump from the 1,000 students across 16 countries who participated in their initial ML demo.cratization tour launched in March 2022.
The strategy breaks down into three tracks. For absolute beginners, there’s the free NLP course built on Hugging Face’s own ecosystem, plus a deep reinforcement learning course where students can publish trained agents to the Hub in one line of code. There’s also a Gradio-focused course on building interactive demos, and a book on Transformers written by Hugging Face staff. The company is explicit about the endgame here: lowering the barrier to becoming a machine learning engineer without charging a dime or running ads.
For instructors, Hugging Face is offering free classroom infrastructure through the Hub, plus a toolkit translated into 8 languages that covers everything from a Hub tour to building and hosting demos with Gradio. They’re running a dedicated workshop on June 6 to teach educators how to use these materials, and they’ve set up a program where university instructors can request a Hugging Face team member to run a session on collaborative ML building for their class.
What’s interesting is how this fits into Hugging Face’s broader open-source strategy. The company has spent years building free tools like transformers and Gradio, and now it’s investing in the educational layer that turns those tools into a talent pipeline. If a meaningful fraction of those 5 million learners graduate into building on the Hub, Hugging Face doesn’t just have a platform—it has a generation of developers whose first ML experience was inside their ecosystem. That’s the kind of moat that’s hard to dislodge, especially when the competition is charging for compute and courses.
