H2O.ai vs. Oracle Machine Learning

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

OAC doesn't require software to be installed since it is browser based. This allows for easier deployment since a local client software is not required to be installed for each user. OAC can be used for the casual light user who mainly consumes data to the power user who can created sophisticated dashboard with advanced analytics. OAC is not meant to replace Essbase reporting.

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

• Analyzing heaps of data dumped into the machine learning tool.
• Giving the researcher an insight on which direction to proceed in order to get the desired results.
• Can help perform a functional analysis before doing a deep dive.

Incentivized

• Better documentation
• Improve the Visual presentations including charting etc

Incentivized

• As mentioned by others the formatting of reports constantly has issues
• Once your initial contract terms are up be prepared for a significant increase
• Pricing needs to be inline with what other competitors are offering

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

Sorry this product was not selected by me, but was a legacy install that was upgraded. I see the value in the product, however, I was not involved in the selection process.

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

• Our ROI has been great since we have been able to get a birdseye view of our business operations.
• Shows your areas within your company that needs attention and improvements.
• Oracle has had a positive impact on all of our business objectives since it provides a clear view of your business operations.

Incentivized