Deltek Collaboration vs. Hugging Face

Deltek Collaboration is best suited for companies and employees that are needing to work closely with other people. Working with agencies, for example, can take so much time waiting to hear back on the status of a project or getting files, so when you can have it all in one place it saves a lot of time and effort.

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

• Integrations with file sharing platforms.
• Project-based spaces making information easy to find.
• Good communication tools.

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

• Pricing could be better as other options are free.
• Improved interface.

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

Before Deltek, we had just communicated around projects through Slack since it was free. It did work well for communication and you could share files, but it is not nearly as robust as Deltek Collaboration. It was not easy to look back and find necessary files, and the collaboration didn't work nearly as well.

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

• Improved communication.
• Centralized location for all documentation.
• Better organization for projects.

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