Caffe Deep Learning Framework vs. Microsoft R Open / Revolution R Enterprise vs. Streamlit

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

If you are a MS shop specifically, or have more generic data requirement needs from Microsoft sourced data this will work well. If you have a lot of disparate data across a number of unique platforms/cloud systems/3rd party hosted data warehouses then this product will have issues or a lack of documentation on the net. Performance-wise this product is equal to other R platforms out there.

Incentivized

- Don't want to pay Tableau $1,000 / seat? Use Streamlit - Want fully custom views and navigation? Use Streamlit - Want access to Machine Learning and not just your dev team? Use Streamlit - Want to keep things internal and secure? Use Streamlit - Want your Data Science team to be able to crank out projects quickly? Use Streamlit - Sick of Jupyter Notebooks and Business Leaders not understanding them? Use Streamlit Our D.S. strategy has moved completely to delivering pages in Streamlit. I can hand an executive a Jupyter notebook and it'll get lost in translation. I can give them sign-in access to a page and they can answer all of their own "What-If?" questions! We've used Streamlit to productize our Data Science and Machine Learning capabilities.

Incentivized

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

Incentivized

• It allows distributed algorithm runs on Hadoop HDFS cluster
• It allows using different file formats such as SAS7BAT files or complex files in tab or comma delimited making data munging easier
• It provides scalable solutions by allowing users to re-use R scripts and distributing the computing over nodes through RHadoop

• Incredibly Easy
• Customizable
• Quick and powerful

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

• Very steep learning curve... for such a quick and useful tool the learning curve is unacceptable.
• Very dangerous in the wrong hands. Because most add-ons are pre-written, you have to trust the community that malicious script is not used.

Incentivized

• Recent Security issues (they quickly released an update to combat this though...)
• Requires a bit of HTML knowledge to really customize. If you're going quick, you don't need HTML though. Streamlit commands will pump your page out fast.

Incentivized

In general, Revolution Analytics brings a lot of value to the organization. The renewal decision would be based on return on investment in terms of quantified actionable insights that are getting generated against the cost of the product. Additionally, market brand of the tool and reputation risk in terms of possible acquisition and its impact to overall organizational analytic strategy would be considered as well.

It is good, easy to use, improvements are being made to the product and more info being shared in the community. It just needs some more time to become more integrated to other platforms and tools/data out there.

Incentivized

Generally support comes through the forums and user generated channels which are helpful, easy to access, quickly turned around and provided by knowledgeable users. However the support channels are not employees and the channels are often used as a way to learn quick difficult elements of R. Better design, users interface and tutorial options would alleviate the need for this sort of interaction.

Incentivized

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

The two are different products for different purposes. But for someone who has little or no experience in R programming, Power BI would be better for starting with. Having said that, Microsoft R is built on R, thus allowing for customization of complex calculations not typically available otherwise.

Incentivized

I started using Streamlit when it first came out and thought it was really useful and powerful. A few years later and they've really hit their stride! The features / widgets / materials they provide have been well researched, well designed, and well implemented. I will take Streamlit to any future companies I go to as well as be a strong promoter wherever I'm currently at. It's free. It's easy to use. It is really powerful. Sure? You could go pay for a larger system but your Data Science team should be able to handle Streamlit easily. I'd argue a non-technical person spending a few weeks in python could pick up Streamlit really quickly.

Incentivized

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

Incentivized

• Helped save company money versus buying other stat software

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• I've scaled my team 2x since using Streamlit. We show off actual results that users can play with
• We're building a customer facing page that we're going to monetize.
• Incredible amounts of visibility into my team and what we're accomplishing.

Incentivized