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H2O.ai OverviewUNIXBusinessApplication

H2O.ai is #15 ranked solution in top Data Science Platforms. IT Central Station users give H2O.ai an average rating of 8 out of 10. H2O.ai is most commonly compared to Dataiku Data Science Studio:H2O.ai vs Dataiku Data Science Studio. The top industry researching this solution are professionals from a computer software company, accounting for 27% of all views.
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.

Buyer's Guide

Download the Data Science Platforms Buyer's Guide including reviews and more. Updated: November 2021

H2O.ai Customers

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

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H2O.ai Reviews

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ArnabSen
Associate Principal at a consultancy with 501-1,000 employees
Real User
Top 10
Good collaboration functionality, but better integration with Python for data science is needed

Pros and Cons

  • "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."
  • "On the topic of model training and model governance, this solution cannot handle ten or twelve models running at the same time."

What is our primary use case?

I am a solution architect and a consultant, and I use H2O as a machine learning platform. I create ensemble models using R and H2O, tune the hyperparameters, and then deploy them.

There are various use cases for this solution. One of the ones I worked on was a trailer forecasting solution. The customer wanted to understand the preload capacity that would be needed to have on hand so that they could call upon the right sized trailers and the right packages. It was a problem of logistics where you had to determine how many trailers were required in order to ship the packages being transported, and also have them ready just in time.

What is most valuable?

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.

What needs improvement?

On the topic of model training and model governance, this solution cannot handle ten or twelve models running at the same time. It becomes a problem.

I would like to see better integration with Python and data science capabilities.

For how long have I used the solution?

I have been using H2O for a long time.

What do I think about the stability of the solution?

We have had no problems with stability.

What do I think about the scalability of the solution?

You can scale it out or scale it up, but that depends on the infrastructure. 

Our customers are Fortune 500 and Fortune 2000 companies.

How are customer service and technical support?

I have not used technical support very much. I've been solving my own problems so I haven't had a need to use them.  I did, however, meet with the H2O guys in Seattle to work on a ticket.

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

I have worked with similar solutions but most of them have been custom. Not a single service could provide us with so many things. For example, H2O plus Spark, or H2O plus Sparkling Water.

How was the initial setup?

The initial setup is straightforward and easy.

The deployment took between two and two and a half hours, but we did not have anybody who was an expert at the time.

What other advice do I have?

H2O is a good product, and I suggest that people use it.

My advice to anybody who is considering this type of solution is to consider whether they want to procure such products and use them versus building something custom. It depends on time availability. It really just exposes Spark as the next layer.

I would rate this solution a seven out of ten.

Disclosure: I am a real user, and this review is based on my own experience and opinions.