We performed a comparison between H2O.ai and RapidMiner based on real PeerSpot user reviews.
Find out in this report how the two Data Science Platforms solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."AutoML helps in hands-free initial evaluations of efficiency/accuracy of ML algorithms."
"Fast training, memory-efficient DataFrame manipulation, well-documented, easy-to-use algorithms, ability to integrate with enterprise Java apps (through POJO/MOJO) are the main reasons why we switched from Spark to H2O."
"The ease of use in connecting to our cluster machines."
"It is helpful, intuitive, and easy to use. The learning curve is not too steep."
"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."
"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 I like about RapidMiner is its all-in-one nature, which allows me to prepare, extract, transform, and load data within the same tool."
"The most valuable feature is what the product sets out to do, which is extracting information and data."
"The solution is stable."
"I've been using a lot of components from the Strategic Extension and Python Extension."
"I like not having to write all solutions from code. Being able to drag and drop controls, enables me to focus on building the best model, without needing to search for syntax errors or extra libraries."
"RapidMiner for Windows is an excellent graphical tool for data science."
"The best part of RapidMiner is efficiency."
"The most valuable features are the Binary classification and Auto Model."
"Referring to bullet-3 as well, H2O DataFrame manipulation capabilities are too primitive."
"The model management features could be improved."
"It lacks the data manipulation capabilities of R and Pandas DataFrames. We would kill for dplyr offloading H2O."
"It needs a drag and drop GUI like KNIME, for easy access to and visibility of workflows."
"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."
"On the topic of model training and model governance, this solution cannot handle ten or twelve models running at the same time."
"I would like to see more features related to deployment."
"RapidMiner would be improved with the inclusion of more machine learning algorithms for generating time-series forecasting models."
"RapidMiner can improve deep learning by enhancing the features."
"I would like to see all users have access to all of the deep learning models, and that they can be used easily."
"Improve the online data services."
"If they could include video tutorials, people would find that quite helpful."
"In terms of the UI and SaaS, the user interface with KNIME is more appealing than RapidMiner."
"RapidMiner isn't cheap. It's a complete solution, but it's costly."
"The server product has been getting updated and continues to be better each release. When I started using RapidMiner, it was solid but not easy to set up and upgrade."
Earn 20 points
H2O.ai is ranked 21st in Data Science Platforms while RapidMiner is ranked 6th in Data Science Platforms with 20 reviews. H2O.ai is rated 7.6, while RapidMiner is rated 8.6. The top reviewer of H2O.ai writes "It is helpful, intuitive, and easy to use. The learning curve is not too steep". On the other hand, the top reviewer of RapidMiner writes "A no-code tool that helps to build machine learning models ". H2O.ai is most compared with Databricks, Amazon SageMaker, Dataiku, Microsoft Azure Machine Learning Studio and Alteryx, whereas RapidMiner is most compared with KNIME, Alteryx, Dataiku and Tableau. See our H2O.ai vs. RapidMiner report.
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