Geckoboard vs. H2O.ai

Great value for the money. Excellent for smaller agencies with multiple projects and teams in a smaller space. We can quickly roll out mobile displays to help with a particular deployment push or monitoring a clients website engagement. It's also useful for showing live data without requiring analytics to run reports from a CRM, etc.

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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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• User self-help - the video tutorials and a robust publicly accessible knowledgebase.
• Geckoboard can aggregate data from a number of other apps into a singular dashboard.
• Sleek, dark UI.

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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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• A bit more flexibility in the number of columns would be nice.
• Ability to scale to mobile device (web).
• App capabilities are missing on Android.

• Better documentation
• Improve the Visual presentations including charting etc

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My company did not renew due to changes in the organization.

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With a simple interface and available templates, creating basic dashboards is easy. Obviously depending on the data you want to visualize, there may be higher learning curves. That being said, they have a huge amount of integrations and extensible frameworks. If you are using anything made in the past ten years there is an API function or integration that can get it talking to the platform. As such, it's pretty easy to hit the main data points you want and get it on a cheap display in front of your team.

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The support levels vary based on the level of plan that you have but that's to be expected. Virtually everything except the Enterprise plan has basic chat/email support. While they are responsive they are not going to be much assistance in helping you figure out API calls or implementing 3rd party integrations. That is to be expected and the support community can pretty much get you in the right direction if you look.

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

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Domo is more robust. We do use both because Geckoboard is really easy to show the rest of our team without the need for additional licenses.

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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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• While we originally used this as an internal IS tool, we eventually have expanded it to be used by nearly every department.
• Because pricing is monthly, we can grow or decrease our usage based on our current client needs.
• Because it is low cost and easy to deploy, we can utilize it in place of considerable resources in analytics and reporting by delivering snapshots of data without pulling reports.

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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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