H2O.ai vs. Mode Analytics

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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Mode is a clear category winner for how it easy it is to go from interactive analysis to professional quality reporting. You can easily do all your SQL and quite a bit of R/Python. If you want to quickly go from your database to simple charts, this is your pick. If you need to do heavier data modeling, scheduling jobs, or ML, you'll need something else, like Databricks. In my opinion, the quality of visualizations and some developer/analyst features make Databricks an unacceptable substitute.

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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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• It allows me to undertake data visualization tasks by creating really insightful charts and dashboards
• It allows for easy sharing and work collaboration on data analysis tasks
• Mode has an easier to use SQL editor as well
• I love how it organizes all our SQL data tables into easy-to-view data we can draw insights on

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• Better documentation
• Improve the Visual presentations including charting etc

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• Date ranges aren't always the most intuitive to set, strangely
• Faster refresh rates would be ideal
• Would love more dynamic mobile device integration (alerts, etc)

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For the advanced user already familiar with Python. It is just the same tool, already managed and connected to the data.

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

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Mode has dedicated agents who are online in a chatroom, ready to take on any questions and provide helpful feedback.

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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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We used both Qlik and Mode at our organization. The mode was used a quick analysis SQL tool while Qlik was our enterprise BI solution.

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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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• Extremely positive. It's a key feature in our application.
• Were it not for the account management bit, Mode would be hands-down our recommendation for anyone looking for cloud BI.

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Independently Invited by TrustRadius

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