We performed a comparison between DataRobot and RapidMiner based on real PeerSpot user reviews.
Find out what your peers are saying about Alteryx, SAP, RapidMiner and others in Predictive Analytics."We especially like the initial part of feature engineering, because feature engineering is included in most engines, but DataRobot has an excellent way of picking up the right features."
"DataRobot can be easy to use."
"It's easy to do MLOps operations. It's a lot easier to manage jobs and see the logs if there's any drift in a model."
"The best part of RapidMiner is efficiency."
"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."
"RapidMiner is very easy to use."
"Scalability is not really a concern with RapidMiner. It scales very well and can be used in global implementations."
"The solution is stable."
"I've been using a lot of components from the Strategic Extension and Python Extension."
"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."
"Using the GUI, I can have models and algorithms drag and drop nodes."
"If we could include our existing Python or R code in DataRobot, we could make it even better. The DataRobot that we have is specific to an industry, but most of the time we would have our own algorithms, which are specific to our own use case. If we had a way by which we could integrate our proprietary things into DataRobot with a simple integration, it would help us a lot."
"Generative AI has taken pace, and I would like to see how DataRobot assists in doing generative AI and large language models."
"The business departments will love to work with DataRobot because they use the tool to investigate their data, such as targeting what they want to investigate. They don't need any data scientists near them. They can investigate at eye level and bring into the BI tool, or can bring it to the data scientist. Data scientists can use this tool to bring increase the solution to the maximum. All the others can use it, but not to the maximum."
"The visual interface could use something like the-drag-and-drop features which other products already support. Some additional features can make RapidMiner a better tool and maybe more competitive."
"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 would like to see all users have access to all of the deep learning models, and that they can be used easily."
"Improve the online data services."
"A great product but confusing in some way with regard to the user interface and integration with other tools."
"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."
"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."
Earn 20 points
DataRobot is ranked 5th in Predictive Analytics with 3 reviews while RapidMiner is ranked 3rd in Predictive Analytics with 20 reviews. DataRobot is rated 8.6, while RapidMiner is rated 8.6. The top reviewer of DataRobot writes "Easy to manage jobs and see the logs if there's any drift in a model, user-friendly, and the data munching is fast". On the other hand, the top reviewer of RapidMiner writes "A no-code tool that helps to build machine learning models ". DataRobot is most compared with Amazon SageMaker, Microsoft Azure Machine Learning Studio, Datadog, Alteryx and SAS Predictive Analytics, whereas RapidMiner is most compared with KNIME, Alteryx, Dataiku, Tableau and Databricks.
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