DataRobot vs RapidMiner comparison

"DataRobot is highly automated, allowing data scientists to build models easily."

"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."

"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."

"RapidMiner is very easy to use."

"The solution is very intuitive and powerful."

"One of the most valuable features is the built-in data tuning feature. Once the model is built, we often struggle to increase its accuracy, but RapidMiner allows us to fine-tune variables. For Example, when working on a project, we can adjust the number of nodes or the depth of trees to see how accuracy changes. This flexibility lets us achieve higher accuracy compared to traditional automated machine-learning models"

"The most valuable features are the Binary classification and Auto Model."

"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."

"The most valuable feature of RapidMiner is that it can read a large number of file formats including CSV, Excel, and in particular, SPSS."

"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."

"I've been using a lot of components from the Strategic Extension and Python Extension."

"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."

"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."

"There are some performance issues."

"Generative AI has taken pace, and I would like to see how DataRobot assists in doing generative AI and large language models."

"The product must provide data-cleaning features."

"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 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."

"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."

"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."

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

"RapidMiner isn't cheap. It's a complete solution, but it's costly."

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

"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."

"The price of DataRobot is good because if you take the price of the solution which is approximately $65,000, it is less than a data scientist. There are very few data scientists available."

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

"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."

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