Fugu vs. Hugging Face

Fugu is a brilliant tool if the employee count is less when it comes to passing on messages within the organization, but it also has some cons with respect to its functioning. The calling feature used to rarely work, making it impossible to make a call when required. Also, the ios version was more prone to crashes.

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

Incentivized

• Communicating within teams.
• Applying for leaves, and attendance was very easy.
• Shortcuts to access everything.

• Model APIs
• Hugging Face Spaces for deploying demo apps
• Latest updated models available easily
• Vast support for language parsing and other relevant tasks

Incentivized

• At times it used to crash making it impossible to use.
• The UI part could have been much better.
• The calling feature was not reliable.

• 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

Fugu is good when it comes to interacting within a small organization; otherwise, not proper working of audio/video calls and notifications not coming on time may cause you to miss out on important information. It has got all the basic employee information available, thus making it very handy and very easy to use.

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.

Incentivized

• As it crashed down more often, productivity was affected at times.
• Keeping a tab on one's leave, and attendance was very easy, making an employee know his balances and scheduling his work accordingly.
• Notification, too, at times, used to not work, thus hampering productivity.

• Hugging Face is cost and time saving.
• Pay is less, you pay what you use, doesn't affect much.
• Overall positive impact on business.

Incentivized