Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research and by community contributors.
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IBM Watson Discovery
Score 9.0 out of 10
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IBM offers Watson Discovery, a natural language processing (NLP) application with options to measure sentiment, detect entities, semantic roles, and other concepts.
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.
Overall, IBM Watson Discovery is an amazing technology that we use with our clients to address various business problems, but the biggest challenge has always been about ingesting, analyzing, enriching, and searching huge collections of documents and allowing our end users and SMEs to be able to search for what they need to reduce the time and efforts spent daily on a manual search through various collections of documents. We have successfully managed to reduce manual work by over 80%, and now our SMEs are being used for the skills they have to gather insights rather than do manual work.
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.
I believe AI should be more flexible about providing data. However, it's understandable that you need to provide the details you need in a more specific and detailed way.
The interface could use more tweaking. Being new to the program, it was kind of hard to navigate.
Luckily, there was a customized feature of the dashboard that I could set up, and having something that you know where you are placed always feels familiar and comfortable.
Similar to all IBM Watson and Salesforce product solutions, the overall support would be a 10/10. Their provided FAQ's help with frequently experienced issues and if still unable to figure something out, their customer service representatives are always super responsive. With instant chat functions available, it is easy to ask a quick question rather than sitting on hold.
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.
Discovery differs from its competitors due to the better ease of implementation and the high level of natural language recognition, it is equal in integration resources such as API and workflow or process pipeline, but it loses in the price for a high volume of documents and/or research. If you own or plan to use other services from the IBM Watson family, there is no doubt that Watson discovery is your best option. Another important point is if you plan to use a cloud or on-premise service (local server or private cloud).
