Compare vs. RapidMiner is ranked 11th in Data Science Platforms with 5 reviews while RapidMiner is ranked 5th in Data Science Platforms with 8 reviews. is rated 7.6, while RapidMiner is rated 8.4. The top reviewer of writes "It is helpful, intuitive, and easy to use. The learning curve is not too steep". 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". is most compared with KNIME, Dataiku Data Science Studio, Amazon SageMaker, Microsoft Azure Machine Learning Studio and IBM Watson Studio, whereas RapidMiner is most compared with KNIME, Alteryx, Dataiku Data Science Studio, IBM SPSS Modeler and SAS Enterprise Miner. See our vs. RapidMiner report.
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Most Helpful Review
Find out what your peers are saying about vs. RapidMiner and other solutions. Updated: July 2020.
430,905 professionals have used our research since 2012.
Quotes From Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

The most valuable features are the machine learning tools, the support for Jupyter Notebooks, and the collaboration that allows you to share it across people.One of the most interesting features of the product is their driverless component. The driverless component allows you to test several different algorithms along with navigating you through choosing the best algorithm.The ease of use in connecting to our cluster machines.It is helpful, intuitive, and easy to use. The learning curve is not too steep.

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The GUI capabilities of the solution are excellent. Their Auto ML model provides for even non-coder data scientists to deploy a model.The best part of RapidMiner is efficiency.Scalability is not really a concern with RapidMiner. It scales very well and can be used in global implementations.The most valuable feature of RapidMiner is that it can read a large number of file formats including CSV, Excel, and in particular, SPSS.The most valuable feature is what the product sets out to do, which is extracting information and data.The most valuable features are the Binary classification and Auto Model.RapidMiner is very easy to use.The documentation for this solution is very good, where each operator is explained with how to use it.

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On the topic of model training and model governance, this solution cannot handle ten or twelve models running at the same time.The interpretability module has room for improvement. Also, it needs to improve its ability to integrate with other systems, like SageMaker, and the overall integration capability.I would like to see more features related to deployment.The model management features could be improved.

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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 think that they should make deep learning models easier.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.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.I would like to see all users have access to all of the deep learning models, and that they can be used easily.The price of this solution should be improved.

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Pricing and Cost Advice
We have seen significant ROI where we were able to use the product in certain key projects and could automate a lot of processes. We were even able to reduce staff.

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The client only has to pay the licensing costs. There are not any maintenance or hidden costs in addition to the license.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 used an educational license for this solution, which is available free of charge.

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H2O is a fully open source, distributed in-memory machine learning platform with linear scalability. H2O’s supports the most widely used statistical & machine learning algorithms including gradient boosted machines, generalized linear models, deep learning and more. H2O also has an industry leading AutoML functionality that automatically runs through all the algorithms and their hyperparameters to produce a leaderboard of the best models. The H2O platform is used by over 14,000 organizations globally and is extremely popular in both the R & Python communities.

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.

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Sample Customers, Stanley Black & Decker, G5, PWC, Comcast, CiscoPayPal, Deloitte, eBay, Cisco, Miele, Volkswagen
Top Industries
Computer Software Company39%
Comms Service Provider12%
Media Company9%
Insurance Company8%
Computer Software Company24%
Comms Service Provider11%
Company Size
Small Business13%
Midsize Enterprise25%
Large Enterprise63%
Small Business55%
Midsize Enterprise9%
Large Enterprise36%
Small Business37%
Midsize Enterprise3%
Large Enterprise60%
Find out what your peers are saying about vs. RapidMiner and other solutions. Updated: July 2020.
430,905 professionals have used our research since 2012.

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