I have use Hugging Face to develop Natural Language Processing applications for other amazon web services customers. Some of the common applications are intelligent document processing, call center support, machine translation, sentiment analysis and so on. These Hugging Face solutions are implemented on the cloud for easier manage and maintain as well.
Pros
Easy to use API
Super well integrated to o cloud
Large community
Cons
Better documentation
Have dedicated support
Likelihood to Recommend
If your organization is looking for a fast turn around development time on natural language processing machine learning use case, and your organization is also well developed on cloud platforms such as google cloud or amazon web services, then implementing Hugging Face into your solution is a very good idea
Hugging Face keeps handy when you work with machine learning projects specially neuronal networks. Neuronal networks are complex and becomes cumbersome when you perform transformation on it. We are resolving this issue with Hugging Face. It has huge amount of libraries with pre-trained models which are optimised too. Hugging Face plays a vital role in machine learning models.
Pros
Great collections of ML libraries (transformers)
Well Documented in multi language.
Perform complex transformations.
Open source driven community
Cons
Libraries documentations can be improved.
sometime hard to select appropriate libraries.
Can add more features.
Likelihood to Recommend
If you or organisation heavily work on machine learning complex neuronal network models Hugging Face has vast libraries transformers who makes the the model easier and efficient. it gives the ability to check the outcome and make changes accordingly and save a lot of time. If you're not working on machine learning neuronal network models it might not appropriate though try it.