IBM Predictive Analytics vs RapidMiner comparison

"The most valuable feature is the predictive capability in marketing use cases."

"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."

"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."

"Scalability is not really a concern with RapidMiner. It scales very well and can be used in global implementations."

"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."

"The data science, collaboration, and IDN are very, very strong."

"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 IBM Predictive Analytics requires more skill, resources, and training than some other solutions."

"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."

"I would like to see more integration capabilities."

"RapidMiner can improve deep learning by enhancing the features."

"A great product but confusing in some way with regard to the user interface and integration with other tools."

"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."

"I think that they should make deep learning models easier."

"The price of this solution should be improved."

"I used an educational license for this solution, which is available free of charge."

"The client only has to pay the licensing costs. There are not any maintenance or hidden costs in addition to the license."

"For the university, the cost of the solution is free for the students and teachers."

"Although we don't pay licensing fees because it is being used within the university, my understanding is that the cost is between $5,000 and $10,000 USD per year."

"I'm not fully aware of RapidMiner's price because we had licenses provided, but from my analysis, it's moderately priced, not too high or too low. It's worth the investment."