H2O.ai Reviews

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Real User
Principal Data Scientist
Apr 05 2018

What is most valuable?

* Fast training * Memory-efficient DataFrame manipulation * Well-documented, easy-to-use algorithms * Ability to integrate with enterprise Java apps These are the main reasons why we switched from Spark to H2O.

How has it helped my organization?

We previously needed a four-machine Spark cluster to be able to train an ML model using tens of millions of transactions, and hours of time during the modeling phase. Currently, same training can now be done on an old MacBook pro with 8GB RAM... more»

What needs improvement?

Referring to bullet-3 as well, H2O DataFrame manipulation capabilities are too primitive.
Associate Consultant at a tech services company with 201-500 employees
May 02 2018

What do you think of H2O.ai?

Primary Use Case Testing/modeling data in the initial stages of approaching a machine-learning problem. Environment: Laptops running Ubuntu 16.04/Python 3. • Valuable Features AutoML helps in hands-free initial evaluations of efficiency/accuracy of ML algorithms; with training input data. • Room for Improvement It needs a drag and drop GUI like KNIME, for easy access to and visibility of workflows. • Use of Solution Less than one year.


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