Caffe Deep Learning Framework vs. OpenText Magellan

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.

Incentivized

It depends on extensibility, set-up, access & style. Actuate performs fairly well, but has performance issues because it sits on top of eclipse, which sits on top of Java. Extensibility usually comes at that price. Set-up is fairly straightforward and it can be secured.

Incentivized

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

Incentivized

• Ability to connect to different kinds of sources, including web services, and customize those queries to the extend needed
• Provide tools to customize the look & feel of the reports as much as needed, even being able to choose different appearances for different outputs
• Allowing scripting in the reports extends the functionality available
• Allowing to embed reports without further licensing and maintaining all the functionality

Incentivized

• 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.

Incentivized

• Report rendering can take a long time for large reports (250,000+ pages) - we run these in batch over night
• The deployment of production code from development to test to production enviroments can be complex due to the number of components that must compile

Incentivized

I am no longer working for the company that was using Actuate but I believe they would continue to use it because the stitching costs would be to high. It would require a complete rewrite of the reports and the never version of Actuate (BIRT) even required an almost complete report rewrite

Incentivized

It is quite intuitive to use. It is fit specifically for doing sentiment, emotion, and intention analysis as well as text classification and text summarization. I would have given 10 if it is fit for the purpose of doing image processing and analysis as well. There is a huge market to analyze video and image data.

Incentivized

Always there on the front and backend for us and the client.

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.

Incentivized

It is vastly superior to these in many ways, for complex reporting it is a much more sophisticated solution. Visualizations are very good. Javascript extensibility is very powerful, others don't support this or as well. Pentaho and MS are both OLAP oriented. Pentaho is moving more toward big data, which was not our primary focus. Others are stuck in the Crystal Reports Band metaphor.

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

Incentivized

• Actuate can handle 50 to 60 sub reports inside a report very well.
• Dynamically creating the datasource, chart, graph, reports are the main advantages. We can do any level of drilling, and can create a performance matrix dashboard efficiently.