Caffe Deep Learning Framework vs. Moveworks

Caffe is only appropriate for some new beginners who don't want to write any lines of code, just want to use existing models for image recognition, or have some taste of the so-called Deep Learning.

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Moveworks is great at automating simple functions with zero human touch and requests, like unlocking user accounts, resetting user passwords, and adding users to DL ticket creation. These functions reduce total calls to our help desk.

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• Caffe is good for traditional image-based CNN as this was its original purpose.

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• Understanding employee questions and serving up information and answers
• Very easy to add new content into the chatbot
• Excellent customer support

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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

• Caffe's model definition - static configuration files are really painful. Maintaining big configuration files with so many parameters and details of many layers can be a really challenging task.
• Besides imagine and vision (CNN), Caffe also gradually adds some other NN architecture support. It doesn't play well in a recurrent domain, so we have to say variety is a problem.
• Caffe's deployment for production is not easy. The community support and project development all mean it is almost fading out of the market.
• The learning curve is quite steep. Although TensorFlow's is not easy to master either, the reward for Caffe is much less than the TensorFlow can offer.

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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

• Conversations, asking users refining questions to better tailor responses

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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

TensorFlow is kind of low-level API most suited for those developers who like to control the details, while Keras provides some kind of high-level API for those users who want to boost their project or experiment by reusing most of the existing architecture or models and the accumulated best practice. However, Caffe isn't like either of them so the position for the user is kind of embarrassing.

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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

• Since we stopped using Caffe before it can reach the production phase, there is no clear ROI that can be defined.

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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

• 22% reduction in calls to support channels
• Employee growth without needing to add support personnel

Incentivized

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