H2O.ai vs. Rational BI

Most suited if in little time you wanted to build and train a model. Then, H2O makes life very simple. It has support with R, Python and Java, so no programming dependency is required to use it. It's very simple to use. If you want to modify or tweak your ML algorithm then H2O is not suitable. You can't develop a model from scratch.

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Definitely well suited for small companies, but again many of their competitors are also well suited in this segment. We were in general happy with the solution provided, but I'm not blown away by their solution or support. If you want to get more data-driven visual boards with data displayed in nice graphics is in my opinion a good start, and here Rational BI delivers what it should.

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• Excellent analytical and prediction tool
• In the beginning, usage of H20 Flow in Web UI enables quick development and sharing of the analytical model
• Readily available algorithms, easy to use in your analytical projects
• Faster than Python scikit learn (in machine learning supervised learning area)
• It can be accessed (run) from Python, not only JAVA etc.
• Well documented and suitable for fast training or self studying
• In the beginning, one can use the clickable Flow interface (WEB UI) and later move to a Python console. There is then no need to click in H20 Flow
• It can be used as open source

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• Arrange data.
• Dashboard.
• Automatic reports.

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• Better documentation
• Improve the Visual presentations including charting etc

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• Not seeing anything at this time, comprehensive product.

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Overall Rational BI is a valuable enterprise reporting tool for any data driven organization. It offers great depth and breadth of features for reporting and analytics that can lead to better business outcomes. Its easy to use and highly configurable to evolve to changing reporting needs of organizations of any scale.

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The overall experience I have with H2O is really awesome, even with its cost effectiveness.

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I haven't used the support myself, but my colleagues have been satisfied with the support. As I have understood from my colleagues the support is as you could expect. Still, the documentation could be better and that could avoid the need to contact their support, but overall we're still happy with the support as well.

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Both are open source (though H2O only up to some level). Both comprise of deep learning, but H2O is not focused directly on deep learning, while Tensor Flow has a "laser" focus on deep learning. H2O is also more focused on scalability. H2O should be looked at not as a competitor but rather a complementary tool. The use case is usually not only about the algorithms, but also about the data model and data logistics and accessibility. H2O is more accessible due to its UI. Also, both can be accessed from Python. The community around TensorFlow seems larger than that of H2O.

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Rational BI allows a deeper data analysis with respect to the other software I experimented with. The velocity to perform the analysis is similar to the other one. The predictive analysis could be very useful, but at the moment I do not use it in my activities. Dashboards are nice and easy to understand.

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• Positive impact: saving in infrastructure expenses - compared to other bulky tools this costs a fraction
• Positive impact: ability to get quick fixes from H2O when problems arise - compared to waiting for several months/years for new releases from other vendors
• Positive impact: Access to H2O core team and able to get features that are needed for our business quickly added to the core H2O product

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• Allows folks to work on other items as reporting is done in the background.
• Scheduling and delivery of reports with one click.
• Single pane of management.
• Ease of use.

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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