H2O.ai vs. Metabase

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.

Incentivized

Metabase is an easy tool to use if you are interested in collecting and aggregating data from multiple platforms. It is also easy to set up and start receiving the data results as a report. It is also easy to integrate with other tools that generate visual reports and take the necessary actions based on the data details.

Incentivized

• 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

Incentivized

• We're currently using it to fetch reports for our products, such as daily sales, pipeline, visits, and attendance reports.
• Data representation from database directly.

Incentivized

• Better documentation
• Improve the Visual presentations including charting etc

Incentivized

• Report Types
• Updates
• Colour/Branding

Incentivized

Its generally quite easy to use but some SQL is definitely important

Incentivized

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

Incentivized

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.

Incentivized

It does not stack up against Metabase, they complete each other.

Incentivized

• 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

Incentivized

• It is serving us whatever we're looking for, and we've recommended many organizations to implement it if they want better data analytics as it provides better functionality than we will build.
• As a negative it gets difficult to get control over data to be fetch initially.

Incentivized