#1 most frequent
United States of America
62.8%384 installations of 611
What is H2O.ai?
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.
Categories & Use Cases
Product Demos
Who Buys & Uses H2O.ai
#2 most frequent
India
5.9%36 installations of 611
#3 most frequent
Canada
3.8%23 installations of 611
Fast machine learning with H2O
Pros
- 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
Cons
- 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.
Return on Investment
- By using H2O the analyst can focus on analysis itself, not spend too much time with coding etc.
- Reuse of algorithms and easy model sharing saves time and money
- An easy learning curve assures low training costs
- By moving to a paid version, even the Driverless AI, you will still need data scientists and analysts, but maybe not so many!
Alternatives Considered
TensorFlow
H2O AutoML superb!!
Pros
- AutoML
- Bigdata support with H2O's Sparkling Water
Cons
- more state of the art algorithm can be added
- Containerization facilities like Docker should be given
Alternatives Considered
KNIME Analytics Platform and RapidMiner Studio
Other Software Used
KNIME Analytics Platform, RapidMiner Studio
Custom H2O implementation
Pros
- Flexible modeling including Ensemble
- Open Source - so that we can know what is really happening and can request changes when needed
- Ability to scale up horizontally by provisioning dynamic clusters
- Access to core development team and speed of problem resolution and feature additions
Cons
- Better documentation
- Improve the Visual presentations including charting etc
Return on Investment
- 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
Alternatives Considered
SAP Predictive Analytics and SAS Advanced Analytics
Other Software Used
SAS Business Intelligence
