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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Microsoft R Open / Revolution R Enterprise
Score8.9 out of 10
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Microsoft R Open and Revolution R Enterprise are big data R distribution for servers, Hadoop clusters, and data warehouses. Microsoft acquired original developer Revolution Analytics in 2016.
Microsoft R is available in two editions: Microsoft R Open (formerly Revolution R Open) and Revolution R Enterprise.
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Pricing
Caffe Deep Learning Framework
Microsoft R Open / Revolution R Enterprise
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Caffe Deep Learning Framework
Microsoft R Open / Revolution R Enterprise
Free Trial
No
No
Free/Freemium Version
No
No
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
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More Pricing Information
Features
Caffe Deep Learning Framework
Microsoft R Open / Revolution R Enterprise
Platform Connectivity
Comparison of Platform Connectivity features of Caffe Deep Learning Framework and Microsoft R Open / Revolution R Enterprise
Feature
Caffe Deep Learning Framework
-
Ratings
Microsoft R Open / Revolution R Enterprise
5.3
3 Ratings
45% below category average
Connect to Multiple Data Sources
00 Ratings
6.13 Ratings
Extend Existing Data Sources
00 Ratings
6.03 Ratings
Automatic Data Format Detection
00 Ratings
6.03 Ratings
MDM Integration
00 Ratings
3.01 Ratings
Data Exploration
Comparison of Data Exploration features of Caffe Deep Learning Framework and Microsoft R Open / Revolution R Enterprise
Feature
Caffe Deep Learning Framework
-
Ratings
Microsoft R Open / Revolution R Enterprise
7.0
3 Ratings
19% below category average
Visualization
00 Ratings
7.03 Ratings
Interactive Data Analysis
00 Ratings
7.03 Ratings
Data Preparation
Comparison of Data Preparation features of Caffe Deep Learning Framework and Microsoft R Open / Revolution R Enterprise
Feature
Caffe Deep Learning Framework
-
Ratings
Microsoft R Open / Revolution R Enterprise
4.8
3 Ratings
52% below category average
Interactive Data Cleaning and Enrichment
00 Ratings
5.13 Ratings
Data Transformations
00 Ratings
5.03 Ratings
Data Encryption
00 Ratings
3.01 Ratings
Built-in Processors
00 Ratings
6.03 Ratings
Platform Data Modeling
Comparison of Platform Data Modeling features of Caffe Deep Learning Framework and Microsoft R Open / Revolution R Enterprise
Feature
Caffe Deep Learning Framework
-
Ratings
Microsoft R Open / Revolution R Enterprise
6.0
3 Ratings
34% below category average
Multiple Model Development Languages and Tools
00 Ratings
5.03 Ratings
Automated Machine Learning
00 Ratings
5.02 Ratings
Single platform for multiple model development
00 Ratings
8.03 Ratings
Self-Service Model Delivery
00 Ratings
6.03 Ratings
Model Deployment
Comparison of Model Deployment features of Caffe Deep Learning Framework and Microsoft R Open / Revolution R Enterprise
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.
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
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.
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
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.
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
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
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.
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
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.
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.
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
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.
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
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
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