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H2O.ai pros and cons

Vendor: H2O.ai
3.8 out of 5
132 followers
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Pros & Cons summary

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Prominent pros & cons

PROS

Fast training and memory-efficient DataFrame manipulation are key benefits of H2O.ai.
Easy integration with enterprise Java applications through POJO/MOJO is a significant advantage.
AutoML facilitates hands-free initial evaluations of machine learning algorithms' efficiency and accuracy.
The driverless component enables testing several algorithms and guides the user in selecting the best one.
Valuable features include machine learning tools and support for Jupyter Notebooks, enabling collaboration across teams.

CONS

H2O DataFrame manipulation capabilities are too primitive, lacking the functionality of R and Pandas DataFrames.
More features related to deployment are needed.
The interpretability module requires improvement and better integration with other systems like SageMaker.
Model management features could be enhanced.
H2O.ai struggles to handle multiple models running simultaneously and can improve in areas like multimodal support and prompt engineering.
 

H2O.ai Pros review quotes

RK
Dec 11, 2018
It is helpful, intuitive, and easy to use. The learning curve is not too steep.
AS
Dec 26, 2019
The most valuable features are the machine learning tools, the support for Jupyter Notebooks, and the collaboration that allows you to share it across people.
MvpOfMac4841 - PeerSpot reviewer
Dec 11, 2018
One of the most interesting features of the product is their driverless component. The driverless component allows you to test several different algorithms along with navigating you through choosing the best algorithm.
Find out what your peers are saying about H2O.ai, Knime, Dataiku and others in Data Science Platforms. Updated: November 2024.
817,751 professionals have used our research since 2012.
Kashif Yaseen - PeerSpot reviewer
Nov 11, 2024
The most valuable feature of H2O.ai is that it is plug-and-play.
it_user837546 - PeerSpot reviewer
Mar 14, 2018
Fast training, memory-efficient DataFrame manipulation, well-documented, easy-to-use algorithms, ability to integrate with enterprise Java apps (through POJO/MOJO) are the main reasons why we switched from Spark to H2O.
DataScie1afc - PeerSpot reviewer
Dec 11, 2018
The ease of use in connecting to our cluster machines.
it_user862530 - PeerSpot reviewer
Apr 25, 2018
AutoML helps in hands-free initial evaluations of efficiency/accuracy of ML algorithms.
 

H2O.ai Cons review quotes

RK
Dec 11, 2018
The model management features could be improved.
AS
Dec 26, 2019
On the topic of model training and model governance, this solution cannot handle ten or twelve models running at the same time.
MvpOfMac4841 - PeerSpot reviewer
Dec 11, 2018
The interpretability module has room for improvement. Also, it needs to improve its ability to integrate with other systems, like SageMaker, and the overall integration capability.
Find out what your peers are saying about H2O.ai, Knime, Dataiku and others in Data Science Platforms. Updated: November 2024.
817,751 professionals have used our research since 2012.
Kashif Yaseen - PeerSpot reviewer
Nov 11, 2024
H2O.ai can improve in areas like multimodal support and prompt engineering.
it_user837546 - PeerSpot reviewer
Mar 14, 2018
Referring to bullet-3 as well, H2O DataFrame manipulation capabilities are too primitive.
DataScie1afc - PeerSpot reviewer
Dec 11, 2018
I would like to see more features related to deployment.
it_user862530 - PeerSpot reviewer
Apr 25, 2018
It needs a drag and drop GUI like KNIME, for easy access to and visibility of workflows.