H2O.ai Archived Reviews (More than two years old)

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Associate Consultant at a tech services company with 201-500 employees
May 02 2018

What do you think of H2O.ai?

What is our primary use case?

Testing/modeling data in the initial stages of approaching a machine-learning problem. Environment: Laptops running Ubuntu 16.04/Python 3.

What is most valuable?

AutoML helps in hands-free initial evaluations of efficiency/accuracy of ML algorithms; with training input data.

What needs improvement?

It needs a drag and drop GUI like KNIME, for easy access to and visibility of workflows.

For how long have I used the solution?

Less than one year.
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 (through POJO/MOJO)… more »

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… more »

What needs improvement?

Referring to bullet-3 as well, H2O DataFrame manipulation capabilities are too primitive.

What's my experience with pricing, setup cost, and licensing?

Currently, we do not purchase enterprise support.

Which solution did I use previously and why did I switch?

We used to developing on Scala + Spark ML. We switched, at least in part, due to reasons mentioned in the Valuable Features section of this review.

What other advice do I have?

We rate it at eight out of 10. It is very fast, light-weight, well-documented, and low-maintenance. The reasons it is not rated 10 are, it lacks the data manipulation… more »

Which other solutions did I evaluate?

We have experience with pretty much everything available; hence, the switch was an informed decision and natural.

What is H2O.ai?

H2O is a fully open source, distributed in-memory machine learning platform with linear scalability. H2O’s supports the most widely used statistical & machine learning algorithms including gradient boosted machines, generalized linear models, deep learning and more. H2O also has an industry leading AutoML functionality that automatically runs through all the algorithms and their hyperparameters to produce a leaderboard of the best models. The H2O platform is used by over 14,000 organizations globally and is extremely popular in both the R & Python communities.

H2O.ai customers

poder.io, Stanley Black & Decker, G5, PWC, Comcast, Cisco

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