H2O.ai Reviews

Name: H2O.ai
Brand: H2O.ai
Rating: 3.8 (8 reviews)

3.8 out of 5

8 reviews
100% willing to recommend

132 followers

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.

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H2O.ai is the #6 ranked solution in top Model Monitoring solutions and #20 ranked solution in top Data Science Platforms. PeerSpot users give H2O.ai an average rating of 7.6 out of 10. H2O.ai is most commonly compared to Databricks: H2O.ai vs Databricks. H2O.ai is popular among the large enterprise segment, accounting for 72% of users researching this solution on PeerSpot. The top industry researching this solution are professionals from a financial services firm, accounting for 20% of all views.

Buyer's Guide

Data Science Platforms

November 2024

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Helped 817,457 peers since 2012

Featured reviews

Kashif Yaseen

Trainee Decision Scientist at a tech services company with 1,001-5,000 employees

We mostly used the solution in the domain that I'm working. We had most of the use cases around chatbots and conversational BI The solution was plug-and-play, meaning most of the components were handled by the solution itself rather than building them from scratch. This was useful for our banking…

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Rahul Koduru

Director of Data Engineering at Transamerica

One example, we are able to automate life insurance. We have to underwrite policies. When somebody applies for a policy, we take their blood, then assign them a risk: substandard, standard, preferred, etc. Depending on this, we price our products. Usually the process is that you take the blood, then it goes to a lab and we get the lab results back, then an underwriter takes a look at the lab results. This is usually done in a two week time frame to get a rating. We were able to build models to automate all of this, and now, it happens in real-time. Somebody can apply online and get issued a policy right away.

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MvpOfMac4841

Managing VP of Machine Learning at a financial services firm with 10,001+ employees

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. I would like more support for scalability and deep learning. Right now, they are very strong in supervise and supervise learning, but not in deep learning. I'd like to see them be more well-rounded, where they have support for deep learning, but I'm not sure that is their business model.

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H2O.ai mindshare

As of December 2024, the mindshare of H2O.ai in the Data Science Platforms category stands at 1.5%, down from 1.6% compared to the previous year, according to calculations based on PeerSpot user engagement data.

Data Science Platforms

Key learnings from peers

Last updated Nov 12, 2024

Valuable Features

"The most valuable feature of H2O.ai is that it is plug-and-play."
"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."
"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."

Room for Improvement

"H2O.ai can improve in areas like multimodal support and prompt engineering."
"On the topic of model training and model governance, this solution cannot handle ten or twelve models running at the same time."
"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."

Pricing

"We have seen significant ROI where we were able to use the product in certain key projects and could automate a lot of processes. We were even able to reduce staff."

These insights are based on the in-depth reviews provided by peers to help you make a better buying decision.

Download our Data Science Platforms Buyer's Guide for additional reliable information.