Hugging Face vs. Plotly Dash

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

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Applicable for data visualization across disciplines. I have used it for data from buildings, building occupancy, public health, and statistics. It is a useful tool to use for big data. It has nice templates and a number of interesting visualization types. If you are familiar with R and python it is easy to use.

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

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• Powerful visualization options.
• Ability to create in-browser interactive visualization apps.
• Ability to create hosted apps.
• Allows you to develop web-based reporting applications without requiring web application development expertise.

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• 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.

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• Would be good if Dashboard Engine was included in the Enterprise VPC plan
• Would love to see ready made fintech apps

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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.

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

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• A no-cost option as it is open sourced.

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