We performed a comparison between DataRobot and RapidMiner based on real PeerSpot user reviews.
Find out in this report how the two Predictive Analytics solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
"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 data science, collaboration, and IDN are very, very strong."
"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 GUI capabilities of the solution are excellent. Their Auto ML model provides for even non-coder data scientists to deploy a model."
"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."
"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."
"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."
"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."
"I would like to see all users have access to all of the deep learning models, and that they can be used easily."
"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."
"In the Mexican or Latin American market, it's kind of pricey."
"RapidMiner isn't cheap. It's a complete solution, but it's costly."
"RapidMiner can improve deep learning by enhancing the features."
"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."
DataRobot captures the knowledge, experience and best practices of the world’s leading data scientists, delivering unmatched levels of automation and ease-of-use for machine learning initiatives. DataRobot enables users to build and deploy highly accurate machine learning models in a fraction of the time.
RapidMiner's unified data science platform accelerates the building of complete analytical workflows - from data prep to machine learning to model validation to deployment - in a single environment, improving efficiency and shortening the time to value for data science projects.
DataRobot is ranked 4th in Predictive Analytics with 2 reviews while RapidMiner is ranked 3rd in Predictive Analytics with 6 reviews. DataRobot is rated 8.0, while RapidMiner is rated 9.0. The top reviewer of DataRobot writes "Has a set of good features and an easy setup". On the other hand, the top reviewer of RapidMiner writes "Extensive features, Turbo Prep, Auto ML, good GUI and good stability". DataRobot is most compared with Alteryx, SAS Predictive Analytics and SAP Analytics Cloud, whereas RapidMiner is most compared with KNIME, Alteryx, Microsoft Azure Machine Learning Studio, Tableau and Dataiku Data Science Studio. See our DataRobot vs. RapidMiner report.
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.