Hugging Face vs. MLReef

Hugging Face is an excellent starting point when working on NLP projects; it is also great for prototyping and developing pipelines for NLP tasks, being those tasks general like embedding representation or specific, like SQUAD models and datasets. It needs more phonetic models or datasets to be as advantageous in that regard.

Incentivized

Works well if you have to involve different roles in different organizations in a project. Less suited when you have a complex system of custom developed tools

• Has access to hundreds of models useful for any NLP usecase.
• Gives better accuracy on prediction tasks.
• Easy to test the model in the website itself to check the accuracy without actually implementing it.
• Has many algorithms for all the prediction problems.

Incentivized

• Helps us to take on more client projects
• Can be used by data analysts as well as casual users

• Most of the Hugging face models are of big size, hence difficult to work if there is no access to high computational system like GPU.
• It’s good to have some visualization tool in hugging face for viewing model architecture.
• I recommend to implement hugging face lite version so that it can run on any system with less specifications.

Incentivized

• Out of the box support for major cloud vendors

Hugging face is the latest technology built using transformers hence it gives better performance than other similar products.

Incentivized

• Reduced the time spent drastically in building complex transformer models
• Very quick deployment of demo apps, that reduces the time spent on making UIs

Incentivized

• We can handle 4 to 6 times more projects at the same time with our team
• We stay engaged with our customers well beyond the project duration