Hugging Face is an open-source provider of natural language processing (NLP) technologies.
$9
per month
Streamlit
Score 8.1 out of 10
N/A
Streamlit is an open-source Python library designed to make it easy to build custom web-apps for machine learning and data science, from the company of the same name in San Francisco. Streamlit also hosts its community's Streamlit Component offered via API to help users get started.
If an organisation has more access to data and have access to high end computers like GPUs it’s recommended to use Hugging face as it will give better accuracy than any other models. If an organisation having less data and has less access to GPUsis looking for decent performance then traditional algorithms are more appropriate than hugging face
- Don't want to pay Tableau $1,000 / seat? Use Streamlit - Want fully custom views and navigation? Use Streamlit - Want access to Machine Learning and not just your dev team? Use Streamlit - Want to keep things internal and secure? Use Streamlit - Want your Data Science team to be able to crank out projects quickly? Use Streamlit - Sick of Jupyter Notebooks and Business Leaders not understanding them? Use Streamlit Our D.S. strategy has moved completely to delivering pages in Streamlit. I can hand an executive a Jupyter notebook and it'll get lost in translation. I can give them sign-in access to a page and they can answer all of their own "What-If?" questions! We've used Streamlit to productize our Data Science and Machine Learning capabilities.
Recent Security issues (they quickly released an update to combat this though...)
Requires a bit of HTML knowledge to really customize. If you're going quick, you don't need HTML though. Streamlit commands will pump your page out fast.
There are some other services offer similar capacity as to Hugging Face, but not entirely the same. For example, amazon web services have a machine learning service called Comprehend, which offer a set of easy to use APIs to do machine translation and entity recognition and some other common NLP use case.
I started using Streamlit when it first came out and thought it was really useful and powerful. A few years later and they've really hit their stride! The features / widgets / materials they provide have been well researched, well designed, and well implemented. I will take Streamlit to any future companies I go to as well as be a strong promoter wherever I'm currently at. It's free. It's easy to use. It is really powerful. Sure? You could go pay for a larger system but your Data Science team should be able to handle Streamlit easily. I'd argue a non-technical person spending a few weeks in python could pick up Streamlit really quickly.
