GitKraken vs. Hugging Face

The software is easy to use and share within our team. Licensing and setup is straightforward. The product just works and we love it.

Incentivized

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

• Customizable workflow steps help organize projects
• Time tracking down to seconds helps to estimate sprints
• Burndown charts let you know when to plan your next release

Incentivized

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

Incentivized

• Timing Out - when editing a story or bug, occasionally Axosoft will time out and all changes will be lost
• Viewing past sprints can be hard to locate
• Multiple users within a Story - Axosoft could do a better job managing multiple users on the same story
• User Interface isn't entirely user friendly and looks a bit outdated

Incentivized

• 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

Once you get the hang of Axosoft, I think it's easy to use however... getting the hang of it takes quite a bit of time. Additionally, once Axosoft makes an update, it can be difficult to find your bearings again. Like I said, once you know how to use the product, I think it's easy but it takes a bit to become comfortable and confident with Axosoft

Incentivized

I actually like JIRA better, to me the UI is a lot cleaner. Easier to use, and search tickets.

Incentivized

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

• Productivity bump for developers over previous software
• Code is never gone forever and can be found if needed
• Allows us to collaborate and finish projects faster

Incentivized

• 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