Users can build custom conversational experiences using Google Assistant’s voice and visual APIs. Take users on journeys through a product, using Assistant’s natural language understanding (NLU) capabilities and developer tools.
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H2O.ai
Score 6.4 out of 10
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An open-source end-to-end GenAI platform for air-gapped, on-premises or cloud VPC deployments. Users can Query and summarize documents or just chat with local private GPT LLMs using h2oGPT, an Apache V2 open-source project. And the commercially available Enterprise h2oGPTe provides information retrieval on internal data, privately hosts LLMs, and secures data.
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Pricing
Google Assistant for Developers
H2O.ai
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Google Assistant for Developers
H2O.ai
Free Trial
No
No
Free/Freemium Version
No
Yes
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
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More Pricing Information
Community Pulse
Google Assistant for Developers
H2O.ai
Considered Both Products
Google Assistant for Developers
Verified User
Anonymous
Chose Google Assistant for Developers
This is where I would go next. And I think in functionality Google Gemini has a place. Especially so in this Newer AI driven world. I don't have any full force desire or usage on Google Gemini at the present. It is mostly what has been passed on to me in indirect ways.
I chose this because it was easier for me and can be accessed via mobile and laptop too because it enables cross device support because it helps in adding more depth to my life, and can help me save tons of time.
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.
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 …
H2O provided all the needed features such as Linear Modeling, Targeted Learning, Predictive Analytics including GLM, Trees, Neural networks and ensemble with ease. We are also able to pick and choose what we want without deploying all the bulky tools unlike others. Able to …
For quick timing and scheduling calls - As I can just say, ok google, I want to have a call with {teammate's name} today, can you find a. time of 30mins for me sometime this week?
Can you answer on the design pattern which Netflix uses for next video
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.
I think newer, complementary ideas are a bit sharper than Google Assistant especially in a Q&A environment or when seeking some depth to a subject. That enhancement is to be expected I feel. And Google Assistant is not so self limiting so I don't have a lot of improvement needs because I use this for what I've become accustomed to and for the ability overall.
It is always important to do your best around hectic places, in bad tower signal areas or even if trying to do something new while using Google Assistant. Have patience in the setting. It pays off.
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.
I feel this can be adjusted and after some trial and error you sort of start knowing what will work and how. And I have to say the overall impact becomes personal and we are all different. I'm small scale and as I've said, it works.
This is where I would go next. And I think in functionality Google Gemini has a place. Especially so in this Newer AI driven world. I don't have any full force desire or usage on Google Gemini at the present. It is mostly what has been passed on to me in indirect ways.
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.
I'm about strategy and clarity in my projects and flexibility to change, enhance and correct. I get what I need in this via Google Assistant.
When I need to get the word out or have a little extra done in real time I can easily use the feature via my voice commands while I'm doing something else simultaneously even in an environment where background noise and atmospheric clutter might be present. Clarity is pretty solid in my experience.
I like to use as little as possible when it comes to Bluetooth, smart enhancements and such and having Google Assistant interact with my connected items is rather smooth as well.
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
