Hugging Face raised $40 million from investors at the end of January, according to a company spokesperson. The move comes as the company looks to become a repository of ready-to-use machine learning models, expand beyond language models, and develop cross-selling MLOps products.
Aims to become GitHub for machine learning
Hugging Face has a mission to become GitHub for machine learning. They started out as a chatbot, but they have since expanded their offerings to include natural language processing, computer vision, and audio. This open source platform allows developers to build AI applications and collaborate with peers.
Hugging Face aims to democratize machine learning and help companies of all sizes create the NLP capabilities they need. The company offers several free and hosted services, as well as a paid suite.
Its business model focuses on providing an infrastructure to train and use thousands of ML models. This is achieved by using an API and AutoTrain technology. In addition, Hugging Face offers a library of pre-trained ML models. These include computer vision and speech models.
Several companies have used the HuggingFace platform to develop a variety of ML models. These include eBay, Pfizer, Intel, and Roche. However, many of the most popular ML models aren’t yet available on the HuggingFace library.
Become central depot for ready-to-use machine-learning models
Hugging Face started out as a chatbot application for bored teens. Originally designed as a messaging application, it was soon adapted to include Natural Language Processing. It has since morphed into a platform for machine learning models. The company has gained a large following among big tech companies.
While Hugging Face may not have made a profit yet, its AI models are being used by major tech firms, including eBay and Intel. Bloomberg pays the company to build and train its model-driven Bing search engine.
Hugging Face aims to be the hub of machine learning. It provides a library of natural language processing (NLP) tools and an interactive online community to help users explore, discover, and learn about ML.
Aside from the library and developer tools, Hugging Face has also created a suite of services that speed up ML model development. This includes a repository of state-of-the-art machine learning models, an API that reduces training time, and a secure cloud service that lets you host models on your own containerized infrastructure.
Expand beyond language models
Hugging Face is a platform for hosting machine learning models. It’s similar to GitHub, in that it provides a hub for users to upload, host and share their projects. However, instead of being a centralized place for users to store and manage data, it’s focused on leveraging open source models to build machine learning applications.
It’s also a great resource for developers looking to learn how to use ML. The Hugging Face library supports more than 100 languages, and it’s built using PyTorch and TensorFlow. Users can access the library with just a few lines of code. And it comes with more than nine thousand datasets.
Hugging Face aims to make ML easier and more accessible. They’ve released an automated training tool that evaluates the best model for the given data. And they’ve boosted security for their customers. Especially in the pharmaceutical industry, they provide enterprise-grade security features to their paying customers.
Hugging Face counts more than one thousand companies as its customers. Some of the largest names include eBay, Intel, Pfizer and Roche.
Develop cross-sell MLOps products
MLOps products provide a range of benefits for businesses. This includes scalability and efficiency, and helps data scientists and development teams work together more effectively. However, there are some risks to consider. Moreover, there’s also a need for companies to develop a strong strategy for MLops.
Despite the advantages of MLops, many businesses have not yet implemented a comprehensive MLops strategy. This is in part due to a lack of standardization and communication. As a result, they’re not achieving the scale they need from their ML models.
To avoid these pitfalls, business leaders should sit down with their executive leadership and come up with a ML roadmap. This will ensure that machine learning development goals align with the expectations of the organization.
To streamline ML model development workflows, companies need to focus on standardization. By combining MLOps with DevOps, enterprises will be able to create higher quality ML models, faster.
Several MLOps solutions address the various components of the ML model lifecycle. These include modeling training, hyperparameter optimization, and plotting tools. Some of these solutions are open source, but some are platform-specific.