- Hugging Face, a popular $2 billion data science tool, has launched its Azure machine learning tool.
- Azure provides another way to get customers who can work with most other providers.
- Hugging Face must grow to a new value of $2 billion, which provides one way to do so.
As competition intensifies to dominate the machine learning industry, Microsoft is turning to popular, $2 billion startups to outperform its competitors.
Azure creators are rolling out integrations with: hug face, a popular data science startup hosting the most popular machine learning models, taking new paths for your company and growing your business. The startup recently raised $100 million worth $2 billion led by Lux Capital in a highly competitive funding round involving Addition and Sequoia.
Microsoft is confident that Hugging Face’s new integration, Endpoints, will help significantly simplify the time required to apply machine learning models. Most efforts in machine learning die before they see the light of day due to the number of people involved. Hugging Face tries to reduce the number to as few as possible by making it easy for one person to share models across the whole. Organization.
“Most machine learning projects never go into production,” Jeff Boudier, product lead at Hugging Face, told Insider. “I think one of the biggest pain points that it contributes is the disconnect between the data science team, the infrastructure team, the compliance team, and the compute resources. To give this permission to a single user, you need to use a tool that does the following: .Already compliant, passing security checks and following processes.”
Even if many of these models aren’t fully adopted in products, even a small introduction to Azure will help Microsoft gain trust among machine learning experts. Companies like Snowflake, which bought small machine learning startups for $800 million, are chasing an increasingly valuable user base.
Boudier said even companies that rely on other cloud providers like Amazon Web Services can quickly deploy machine learning models in Azure that other services can call. For example, a search engine running in another cloud can quickly call a model trained and stored in Azure to provide personalized results.
Strategic relationships allow Hugging Face to formalize relationships with many companies that use Hugging Face more ad hoc, along with models saved by startups. Microsoft, meanwhile, is wedged within its data science team, which wants to export models and doesn’t want to deal with the friction that usually accompanies.
It’s understandable that companies like Hugging Face would like to expand their entire base with an integration like this, but this move rarely happens in a vacuum. Microsoft has an incentive to become more popular in the machine learning community as companies like Amazon and Snowflake invest aggressively in competing offerings. The Hugging Face provides a strategic opportunity for the cloud giants.
“This partnership further deepens our shared aspiration to make it easier for enterprise data science teams to get started with Hugging Face on Azure,” said Jamal Robinson, Senior Director, AI Platforms and Services Business Development. Furthering our goal of providing our customers with the most comprehensive Hugging Face platform in the cloud, and making it easier for software developers, citizen data scientists, and advanced machine learning practitioners on Azure to access cutting-edge NLP, audio and computer vision models. develop.”
Hugging Face is used by machine learning practitioners to download and run popular machine learning models such as OpenAI’s GPT-2 and Google’s BERT. Both allow you to quickly and easily start analyzing large blocks of text without spending time and resources training a machine learning model. Hugging Face also has thousands of models spanning different use cases such as computer vision and audio.