We performed a comparison between Cloudera Data Science Workbench and RapidMiner based on real PeerSpot user reviews.
Find out what your peers are saying about Databricks, Microsoft, Alteryx and others in Data Science Platforms."I appreciate CDSW's ability to logically segregate environments, such as data, DR, and production, ensuring they don't interfere with each other. The deployment of machine learning is fast and easy to manage. Its API calls are also fast."
"The Cloudera Data Science Workbench is customizable and easy to use."
"I like not having to write all solutions from code. Being able to drag and drop controls, enables me to focus on building the best model, without needing to search for syntax errors or extra libraries."
"The data science, collaboration, and IDN are very, very strong."
"Using the GUI, I can have models and algorithms drag and drop nodes."
"Scalability is not really a concern with RapidMiner. It scales very well and can be used in global implementations."
"The solution is stable."
"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."
"RapidMiner is very easy to use."
"I've been using a lot of components from the Strategic Extension and Python Extension."
"Running this solution requires a minimum of 12GB to 16GB of RAM."
"The tool's MLOps is not good. It's pricing also needs to improve."
"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."
"In terms of the UI and SaaS, the user interface with KNIME is more appealing than RapidMiner."
"RapidMiner isn't cheap. It's a complete solution, but it's costly."
"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 appreciate improvements in automation and customization options to further streamline processes."
"If they could include video tutorials, people would find that quite helpful."
"The price of this solution should be improved."
"A great product but confusing in some way with regard to the user interface and integration with other tools."
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Cloudera Data Science Workbench is ranked 17th in Data Science Platforms with 2 reviews while RapidMiner is ranked 7th in Data Science Platforms with 19 reviews. Cloudera Data Science Workbench is rated 7.0, while RapidMiner is rated 8.6. The top reviewer of Cloudera Data Science Workbench writes "Useful for data science modeling but improvement is needed in MLOps and pricing ". 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". Cloudera Data Science Workbench is most compared with Databricks, Amazon SageMaker, Microsoft Azure Machine Learning Studio, Dataiku Data Science Studio and Google Cloud Datalab, whereas RapidMiner is most compared with KNIME, Alteryx, Dataiku Data Science Studio, Tableau and Microsoft Azure Machine Learning Studio.
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