We performed a comparison between H2O.ai and IBM SPSS Modeler based on real PeerSpot user reviews.
Find out what your peers are saying about Databricks, Microsoft, Alteryx and others in Data Science Platforms."It is helpful, intuitive, and easy to use. The learning curve is not too steep."
"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."
"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."
"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."
"AutoML helps in hands-free initial evaluations of efficiency/accuracy of ML algorithms."
"We are using it either for workforce deployment or to improve our operations."
"We have been able to do some predictive modeling with it"
"The ease of use in the user interface is the best part of it. The ability to customize some of my streams with R and Python has been very useful to me, I've automated a few things with that."
"We have a local representative who specializes in SPSS. He will help us do the PoC."
"Extremely easy to use, it offers a generous selection of proprietary machine learning algorithms."
"Our business units' capabilities with SPSS Modeler is high. They no longer waste time on modeling and algorithms, meaning they are not coding any more. For example, segmentation projects now take one to three months, rather than six months to a year, as before."
"It is just a lot faster. So you do not have to write a bunch of code, you can throw that stuff on there pretty quickly and do prototyping quickly."
"Automation is great and this product is very organized."
"It lacks the data manipulation capabilities of R and Pandas DataFrames. We would kill for dplyr offloading H2O."
"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."
"I would like to see more features related to deployment."
"Referring to bullet-3 as well, H2O DataFrame manipulation capabilities are too primitive."
"On the topic of model training and model governance, this solution cannot handle ten or twelve models running at the same time."
"The challenge for the very technical data scientists: It is constraining for them."
"Initial setup of the software was complex, because of our own problems within the government."
"I understand that it takes some time to incorporate some of the new algorithms that have come out in the last few months, in the literature. For example, there is an algorithm based on how ants search for food. And there are some algorithms that have now been developed to complement rules. So that's one of the things that we need to have incorporated into it."
"Dimension reduction should be classified separately."
"Customer support is hard to contact."
"The product does not have a search function for tags."
"The platform's cloud version needs improvements."
"C&DS will not meet our scalability needs."
Earn 20 points
H2O.ai is ranked 20th in Data Science Platforms while IBM SPSS Modeler is ranked 12th in Data Science Platforms with 38 reviews. H2O.ai is rated 7.6, while IBM SPSS Modeler is rated 8.0. 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 IBM SPSS Modeler writes "Easy to use, quick to learn, and offers many ways to analyze data". H2O.ai is most compared with Databricks, Amazon SageMaker, Dataiku Data Science Studio, Microsoft Azure Machine Learning Studio and KNIME, whereas IBM SPSS Modeler is most compared with KNIME, Microsoft Power BI, RapidMiner, IBM SPSS Statistics and Alteryx.
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