We performed a comparison between IBM Predictive Analytics and RapidMiner based on real PeerSpot user reviews.
Find out what your peers are saying about Alteryx, SAP, RapidMiner and others in Predictive Analytics."The most valuable feature is the predictive capability in marketing use cases."
"The solution is stable."
"The most valuable feature is what the product sets out to do, which is extracting information and data."
"It's helpful if you want to make informed decisions using data. We can take the information, tease out the attributes, and label everything. It's suitable for profiling and forecasting in any industry."
"I've been using a lot of components from the Strategic Extension and Python Extension."
"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."
"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."
"Using the GUI, I can have models and algorithms drag and drop nodes."
"We value the collaboration and governance features because it's a comprehensive platform that covers everything from data extraction to modeling operations in the ML language. RapidMiner is competitive in the ML space."
"Using IBM Predictive Analytics requires more skill, resources, and training than some other solutions."
"In terms of the UI and SaaS, the user interface with KNIME is more appealing than RapidMiner."
"I would appreciate improvements in automation and customization options to further streamline processes."
"RapidMiner would be improved with the inclusion of more machine learning algorithms for generating time-series forecasting models."
"Many things in the interface look nice, but they aren't of much use to the operator. It already has lots of variables in there."
"I think that they should make deep learning models easier."
"One challenge I encountered while implementing RapidMiner was the lack of documentation. Since there aren't as many users, finding resources to learn the tool was initially difficult. To overcome this hurdle, I believe RapidMiner could improve by providing more tutorials tailored for new users."
"It would be helpful to have some tutorials on communicating with Python."
"I would like to see more integration capabilities."
Earn 20 points
IBM Predictive Analytics is ranked 22nd in Predictive Analytics while RapidMiner is ranked 3rd in Predictive Analytics with 20 reviews. IBM Predictive Analytics is rated 7.0, while RapidMiner is rated 8.6. The top reviewer of IBM Predictive Analytics writes "Good prediction capability for marketing purposes, although it needs to be more flexible". On the other hand, the top reviewer of RapidMiner writes "A no-code tool that helps to build machine learning models ". IBM Predictive Analytics is most compared with , whereas RapidMiner is most compared with KNIME, Alteryx, Dataiku, Tableau and Microsoft Azure Machine Learning Studio.
See our list of best Predictive Analytics vendors.
We monitor all Predictive Analytics 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.
