H2O.ai vs. Oracle Digital Assistant

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

As mentioned in pros and cons, it depends on the use cases. Most of the normal chatbot use cases can be handled by ODA. If you want to build a chatbot with menu style or conversation style (it is not straight forward but it can be done), ODA can be perfect. If use cases are to also include AI with emotional intelligence to make conversational more interactive and also be able to detect through AI engine automatically what a person would like to do or perform or ask, then ODA may not fit for it. If you also are looking for virtual agents through tighter IVR integration then ODA may not be right. There are a few limitations around the number of words in the text to voice feature.

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

• Oracle AI processes natural language well and the conversations pick a good flow.
• It recognizes pattern of behavior for more refined results with each implementation under machine learning.

Incentivized

• Better documentation
• Improve the Visual presentations including charting etc

Incentivized

• Our clients have reported that upon integration with Siri for voice commands, the results are pretty obvious (basic) Nd the conversation is unnatural.
• The languages it supports are very limited.

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

I selected Oracle Digital Assistant against all other digital assistant platforms as this platform works like a charm with any Oracle application. It integrates well with Oracle Integration Cloud. The new beta version has an inbuilt conversation builder which can be used to build conversation without the YAML code.

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

• We've had a good integration with our existing databases and applications.
• Our customers are crypto traders and we've had more engagement with them, which means a lot to us.

Incentivized