RajivSharmaSenior Product Manager at CustomerXps Software
We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
"All of the features of this product are quite good."
"The documentation for this solution is very good, where each operator is explained with how to use it."
"The most valuable features are the Binary classification and Auto Model."
"The most valuable feature is what the product sets out to do, which is extracting information and data."
"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 best part of RapidMiner is efficiency."
"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 interface should be more user-friendly."
"The price of this solution should be improved."
"RapidMiner would be improved with the inclusion of more machine learning algorithms for generating time-series forecasting models."
"A great product but confusing in some way with regard to the user interface and integration with other tools."
"It would be helpful to have some tutorials on communicating with Python."
"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."
"I think that they should make deep learning models easier."
"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 like to see all users have access to all of the deep learning models, and that they can be used easily."
"I used an educational license for this solution, which is available free of charge."
"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."
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Cloud Datalab is a powerful interactive tool created to explore, analyze, transform and visualize data and build machine learning models on Google Cloud Platform. It runs on Google Compute Engine and connects to multiple cloud services easily so you can focus on your data science tasks.
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.
Google Cloud Datalab is ranked 19th in Data Science Platforms with 1 review while RapidMiner is ranked 7th in Data Science Platforms with 9 reviews. Google Cloud Datalab is rated 8.0, while RapidMiner is rated 8.4. The top reviewer of Google Cloud Datalab writes "Stable, feature-rich, and easy to set up". 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". Google Cloud Datalab is most compared with Databricks, IBM Watson Studio, Microsoft Azure Machine Learning Studio, Cloudera Data Science Workbench and MathWorks Matlab, whereas RapidMiner is most compared with KNIME, Alteryx, Dataiku Data Science Studio, Tableau and Microsoft Azure Machine Learning Studio.
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We monitor all Data Science Platforms 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.