Deep Dive on Hugging Face: The $2 Billion Open Source Company.

Good Morning AI Runners

Today we have a deep dive on Hugging Face, the open-source platform and provider of machine learning technologies worth an estimated $2 Billion.

Deep Dive on Hugging Face: The $2 Billion Open Source Company

Hugging Face is a technology company that started as a messaging app with chatbots for bored teenagers. However, the consumer bet did not pay off and the company pivoted to focusing on natural language processing technology (NLP).

NLP is a way to make sense of textual data and is used in many applications, such as voice-driven assistants, natural-language search, question answering, sentiment analysis for automated trading, business intelligence, social media analytics, and content summarization. Some examples of products that use NLP are Alexa, Cortana, and Siri. Many organizations are looking to integrate NLP into their workflows and products they provide such as translation, speech recognition and chatbots.

Hugging Face began its pivot and first step to fame by releasing the Transformers library on GitHub, which allows users to leverage popular NLP models like BERT and GPT-3 for tasks like text classification, information extraction, question answering, text summarization, and text generation.

Transformers or transformer models are a type of deep learning architecture that has been behind many recent advances in artificial intelligence, including large language models like OpenAI GPT-3 and DeepMind’s protein-folding model AlphaFold.

Large tech companies like Google, Facebook, and Microsoft have been using transformer models for several years. But the past couple of years has seen a growing interest in transformers among smaller companies, including many that don’t have in-house machine learning talent.

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.

The company recently secured $100 million in Series C funding and has a valuation of $2 billion. It plans to double down on research, open-source, products, and the "responsible democratization" of AI. Over 10,000 companies are currently using Hugging Face's tools and services.

How does Hugging Face make money?

The company makes money through a freemium model, with most of its product offerings being free and accessible to all users on the platform. It has recently started monetizing with two premium offerings: "Expert Support" and "Private Model Hub," which help data scientists and machine learning engineers save time and accelerate their machine learning roadmaps.

Run The AI: PPPs

Pick up (learn):

Intro to Machine Learning course by the founder of Udacity, Sebastian Thrun. (scroll down to learn more about him)

Pilot (play):

Comprehensive database of AIs for different tasks:

Person:

Sebastian Thrun, is a German-American entrepreneur and computer scientist. He is CEO of Kitty Hawk Corporation, and chairman and co-founder of Udacity. Before that, he was a Google VP and Fellow, a Professor of Computer Science at Stanford University, and before that at Carnegie Mellon University. At Google, he founded Google X and Google's self-driving car team.

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