We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
"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 most valuable feature of RapidMiner is that it can read a large number of file formats including CSV, Excel, and in particular, SPSS."
"Scalability is not really a concern with RapidMiner. It scales very well and can be used in global implementations."
"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."
"The GUI capabilities of the solution are excellent. Their Auto ML model provides for even non-coder data scientists to deploy a model."
"RapidMiner is very easy to use."
"The data science, collaboration, and IDN are very, very strong."
"The best part of RapidMiner is efficiency."
"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."
"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 would like to see all users have access to all of the deep learning models, and that they can be used easily."
"A great product but confusing in some way with regard to the user interface and integration with other tools."
"RapidMiner would be improved with the inclusion of more machine learning algorithms for generating time-series forecasting models."
"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."
"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."
"It would be helpful to have some tutorials on communicating with Python."
"I think that they should make deep learning models easier."
"We dropped the plan to use DataRobot, because we found the pricing to be on the higher sise. We liked DataRobot a lot, but due to the pricing, we dropped that idea."
"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."
"The client only has to pay the licensing costs. There are not any maintenance or hidden costs in addition to the license."
Earn 20 points
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 1 review while RapidMiner is ranked 3rd in Predictive Analytics with 9 reviews. DataRobot is rated 8.0, while RapidMiner is rated 8.6. 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 "Offers good tutorials that make it easy to learn and use, with a powerful feature to compare machine learning algorithms". 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, Dataiku Data Science Studio and Tableau.
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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.