H2O.ai Overview

H2O.ai is the #14 ranked solution in our list of top Data Science Platforms. It is most often compared to KNIME: H2O.ai vs KNIME

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 Buyer's Guide

Download the H2O.ai Buyer's Guide including reviews and more. Updated: January 2021

H2O.ai Customers

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

H2O.ai Video

H2O.ai Reviews

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ArnabSen
Associate Principal at a consultancy with 501-1,000 employees
Real User
Top 20
Dec 26, 2019
Good collaboration functionality, but better integration with Python for data science is needed

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… more »

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 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.
reviewer1007100
Supervisor in Research and Development Area with 1,001-5,000 employees
Real User
Top 20
Feb 14, 2019
We're hoping to save costs on internal development but keep enough flexibility to choose ML techniques and performance indicators

What is our primary use case?

The idea is to migrate the current model's development practice to another platform. Then after, try to create a proprietary platform using R and Python. The company is interested in using an external platform in order to have an updated environment.

How has it helped my organization?

Still on it. The idea is to save the cost of internal development but keeping enough flexibility to choose ML techniques and performance indicators.

What is most valuable?

Still on it.

What needs improvement?

Feature engineering.

For how long have I used the solution?

Still implementing.