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."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."
"DataRobot can be easy to use."
"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."
"The GUI capabilities of the solution are excellent. Their Auto ML model provides for even non-coder data scientists to deploy a model."
"The documentation for this solution is very good, where each operator is explained with how to use it."
"The solution is very intuitive and powerful."
"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."
"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 most valuable features are the Binary classification and Auto Model."
"The best part of RapidMiner is efficiency."
"RapidMiner is a no-code machine learning tool. I can install it on my local machine and work with smaller datasets. It can also connect to databases, allowing me to build models directly on the data stored there. RapidMiner offers a wider range of operators than other tools like Dataiku, making it a better option for my needs."
"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."
"In the Mexican or Latin American market, it's kind of pricey."
"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."
"RapidMiner would be improved with the inclusion of more machine learning algorithms for generating time-series forecasting models."
"Improve the online data services."
"I would like to see all users have access to all of the deep learning models, and that they can be used easily."
"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."
"If they could include video tutorials, people would find that quite helpful."
DataRobot is ranked 5th in Predictive Analytics with 3 reviews while RapidMiner is ranked 3rd in Predictive Analytics with 21 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.
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.
