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."The ease of use in connecting to our cluster machines."
"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."
"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."
"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."
"The visual modeling capability is one of its attractive features."
"Stability is good."
"We are using it either for workforce deployment or to improve our operations."
"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 use analytics with the visual modeling capability to leverage productivity improvements."
"It’s definitely scalable, it’s all on the same platform, it’s well integrated. I think the integration is important in terms of scalablility because essentially, having the entire suite helps a lot to scale it"
"It scales. I have not run into any challenges where it will not perform."
"The supervised models are valuable. It is also very organized and easy to use."
"The model management features could be improved."
"I would like to see more features related to deployment."
"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."
"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."
"On the topic of model training and model governance, this solution cannot handle ten or twelve models running at the same time."
"It lacks the data manipulation capabilities of R and Pandas DataFrames. We would kill for dplyr offloading H2O."
"Time Series or forecasting needs to be easier. It is a very important feature, and it should be made easier and more automated to use. For instance, for logistic regression, binary or multinomial is used automatically based on the type of the target variable. I wish they can make Time Series easier to use in a similar way."
"It would be good if IBM added help resources to the interface."
"Requires more development."
"The standard package (personal) is not supported for database connection."
"I can say the solution is outdated."
"The time series should be improved."
"I would not rate the technical support very well. The technicians have accents. When you do find someone, it is very hard to get somebody able to answer the technical questions."
"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."
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, 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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