We performed a comparison between H2O.ai and RapidMiner based on real PeerSpot user reviews.
Find out what your peers are saying about Databricks, Microsoft, Alteryx and others in Data Science Platforms."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."
"AutoML helps in hands-free initial evaluations of efficiency/accuracy of ML algorithms."
"The ease of use in connecting to our cluster machines."
"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."
"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 feature is what the product sets out to do, which is extracting information and data."
"The most valuable features are the Binary classification and Auto Model."
"The best part of RapidMiner is efficiency."
"It is easy to use and has a huge community that I can rely on for help. Moreover, it is interactive."
"The most valuable feature of RapidMiner is that it is code free. It is similar to playing with Lego pieces and executing after you are finished to see the results. Additionally, it is easy to use and has interesting utilities when preparing the data. It has a utility to automatically launch a series of models and show the comparisons. When finished with the comparisons you can select the best one, and deploy it automatically."
"The data science, collaboration, and IDN are very, very strong."
"I've been using a lot of components from the Strategic Extension and Python Extension."
"The documentation for this solution is very good, where each operator is explained with how to use it."
"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."
"The model management features could be improved."
"It needs a drag and drop GUI like KNIME, for easy access to and visibility of workflows."
"Referring to bullet-3 as well, H2O DataFrame manipulation capabilities are too primitive."
"I would like to see more features related to deployment."
"It lacks the data manipulation capabilities of R and Pandas DataFrames. We would kill for dplyr offloading H2O."
"The price of this solution should be improved."
"I would like to see all users have access to all of the deep learning models, and that they can be used easily."
"RapidMiner would be improved with the inclusion of more machine learning algorithms for generating time-series forecasting models."
"In terms of the UI and SaaS, the user interface with KNIME is more appealing than RapidMiner."
"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."
"In the Mexican or Latin American market, it's kind of pricey."
"It would be helpful to have some tutorials on communicating with Python."
"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."
Earn 20 points
H2O.ai is ranked 19th in Data Science Platforms while RapidMiner is ranked 10th in Data Science Platforms with 19 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 "Easy to use, robust, and simple to deploy". H2O.ai is most compared with Amazon SageMaker, Databricks, Dataiku Data Science Studio, Microsoft Azure Machine Learning Studio and IBM Watson Studio, whereas RapidMiner is most compared with KNIME, Alteryx, Dataiku Data Science Studio, Tableau and Anaconda.
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