Cloudera Data Platform vs. Hugging Face

I have seen that Cloudera Data Platform is well suited for large batch processes. It works really well for our indication analyses that are performed by the actuaries. I feel that rapid streaming operations may be a situation where additional technology would be needed to provide for a robust solution.

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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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• Scales
• Highly available

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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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• Constantly changing costs
• Log visibility

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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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We have utilized Cloudera support quite frequently and are very satisfied with the capability and responsiveness of that team. Often, the new features delivered with the platform give us an opportunity to mature the way we're doing things, and the support team have been valuable in developing those new patterns.

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IBM's offering of the Cloud Pak for Data has been a moving target and difficult to compare to Cloudera Data Platform. We have implemented our solution on Amazon Web Services, which appears to be supported by IBM at this point, but the migration would be very expensive for us to endeavor.

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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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• Reduced operational costs
• Speed to market

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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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Independently Invited by TrustRadius

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info