H2O.ai vs. Oracle Digital Assistant

Use H2O.ai whenever you need easy to use tool, when you must be cost efficient (you can not charge the client extra money for software licenses used), need a tool with lots of algorithms that are normally used in data analytics, or need to work on one machine (it is either not allowed to move data to cloud storage or simply not necessary to connect to Hadoop, etc.). Also, you can call H2O directly from Python which makes analysis more efficient.

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

• AutoML
• Bigdata support with H2O's Sparkling Water

Incentivized

• Developing new and custom skills for chatbots.
• Developing conversational style skills for chatbots.
• Menu style skills for chatbots.
• Lots of skills those are available out of the box.

Incentivized

• No weaknesses found yet
• This is not really a drawback, but rather a warning - the Drivereless AI is not a replacement for a data scientist yet, and will not replace data scientists in the next decade neither. The Driverless AI feature delivers reliable results only if the analyst is sure about the meaning of input data. The data quality is usually a major issue and no tool can detect the meaning of data in the input. Data scientists are also required for business interpretation of the findings. So be careful, and do not rely on this feature without a good understanding of what it really does in each step.

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

I have used Knime, RapidMiner, and Weka before I heard about H2O, but amongst all I really liked H2O. However, nowadays Googles AutoML and AWS SageMaker AutoML platform are really competitive, but more costly than 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