Compare H2O.ai vs. RapidMiner

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H2O.ai Logo
7,485 views|4,953 comparisons
RapidMiner Logo
8,868 views|6,948 comparisons
Most Helpful Review
Find out what your peers are saying about Alteryx, Databricks, Knime and others in Data Science Platforms. Updated: June 2021.
511,521 professionals have used our research since 2012.
Quotes From Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros
"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."

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"The documentation for this solution is very good, where each operator is explained with how to use it.""The most valuable features are the Binary classification and Auto Model.""The most valuable feature is what the product sets out to do, which is extracting information and data.""The most valuable feature of RapidMiner is that it can read a large number of file formats including CSV, Excel, and in particular, SPSS.""Scalability is not really a concern with RapidMiner. It scales very well and can be used in global implementations.""The best part of RapidMiner is efficiency.""The GUI capabilities of the solution are excellent. Their Auto ML model provides for even non-coder data scientists to deploy a model.""RapidMiner is very easy to use."

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

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"The price of this solution should be improved.""RapidMiner would be improved with the inclusion of more machine learning algorithms for generating time-series forecasting models.""A great product but confusing in some way with regard to the user interface and integration with other tools.""It would be helpful to have some tutorials on communicating with Python.""The visual interface could use something like the-drag-and-drop features which other products already support. Some additional features can make RapidMiner a better tool and maybe more competitive.""I think that they should make deep learning models easier.""The biggest problem, not from a platform process, but from an avoidance process, is when you work in a heavily regulated environment, like banking and finance. Whenever you make a decision or there is an output, you need to bill it as an avoidance to the investigator or to the bank audit team. If you made decisions within this machine learning model, you need to explain why you did so. It would better if you could explain your decision in terms of delivery. However, this is an issue with all ML platforms. Many companies are working heavily in this area to help figure out how to make it more explainable to the business team or the regulator.""I would like to see all users have access to all of the deep learning models, and that they can be used easily."

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Pricing and Cost Advice
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"I used an educational license for this solution, which is available free of charge.""Although we don't pay licensing fees because it is being used within the university, my understanding is that the cost is between $5,000 and $10,000 USD per year.""The client only has to pay the licensing costs. There are not any maintenance or hidden costs in addition to the license."

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511,521 professionals have used our research since 2012.
Ranking
14th
Views
7,485
Comparisons
4,953
Reviews
1
Average Words per Review
475
Rating
7.0
7th
Views
8,868
Comparisons
6,948
Reviews
8
Average Words per Review
660
Rating
8.5
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Overview

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.

RapidMiner's unified data science platform accelerates the building of complete analytical workflows - from data prep to machine learning to model validation to deployment - in a single environment, improving efficiency and shortening the time to value for data science projects.

Offer
Learn more about H2O.ai
Learn more about RapidMiner
Sample Customers
poder.io, Stanley Black & Decker, G5, PWC, Comcast, Cisco
PayPal, Deloitte, eBay, Cisco, Miele, Volkswagen
Top Industries
VISITORS READING REVIEWS
Computer Software Company31%
Comms Service Provider15%
Financial Services Firm8%
Media Company6%
REVIEWERS
University43%
Energy/Utilities Company14%
Educational Organization14%
Engineering Company14%
VISITORS READING REVIEWS
Computer Software Company22%
Comms Service Provider21%
University9%
Government7%
Company Size
REVIEWERS
Small Business13%
Midsize Enterprise25%
Large Enterprise63%
REVIEWERS
Small Business62%
Midsize Enterprise8%
Large Enterprise31%
VISITORS READING REVIEWS
Small Business31%
Midsize Enterprise3%
Large Enterprise66%
Find out what your peers are saying about Alteryx, Databricks, Knime and others in Data Science Platforms. Updated: June 2021.
511,521 professionals have used our research since 2012.

H2O.ai is ranked 14th in Data Science Platforms with 1 review while RapidMiner is ranked 7th in Data Science Platforms with 9 reviews. H2O.ai is rated 7.0, while RapidMiner is rated 8.4. The top reviewer of H2O.ai writes "Good collaboration functionality, but better integration with Python for data science is needed". On the other hand, the top reviewer of RapidMiner writes "Offers good tutorials that make it easy to learn and use, with a powerful feature to compare machine learning algorithms". H2O.ai is most compared with KNIME, Dataiku Data Science Studio, Amazon SageMaker, Microsoft Azure Machine Learning Studio and Cloudera Data Science Workbench, whereas RapidMiner is most compared with KNIME, Alteryx, Dataiku Data Science Studio, Tableau and Microsoft BI.

See our list of best Data Science Platforms vendors.

We monitor all Data Science Platforms reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.