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RapidMiner is a Leader in the 2018 Gartner Magic Quadrant for Data Science and Machine Learning Platforms

According to Gartner “Data science and machine learning platforms enable organizations to take an end-to-end approach to building and deploying data science models.”

In this new report, Gartner has named RapidMiner a leader for the fifth year in a row.

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    RapidMiner makes analytics teams more productive through an open and extensible data science platform. RapidMiner unifies the entire data science lifecycle, from data prep to machine learning to predictive model deployment. Over 350,000 analysts and data scientists use RapidMiner products to drive revenue, reduce costs, and avoid risks.

    Gartner identified the following strengths when evaluating RapidMiner. Read the full report for more on RapidMiner and all of the other vendors.

    • Positive market response: RapidMiner focuses equally on data scientists and citizen data scientists — it aims for completeness and simplicity in its offerings. Reference customers expressed satisfaction with how RapidMiner’s offerings meet their needs. Accordingly, RapidMiner’s revenue has grown impressively, year over year.
    • Model factory and model development: RapidMiner provides deep and broad modeling capabilities for automated end-to-end model development. For example, RapidMiner Studio includes a visual workflow designer and guided analytics. RapidMiner’s process execution framework provides a flexible, extensible and scalable capability for running and monitoring large numbers of model processes. RapidMiner supports automatic retraining of models, based on newer data. Marketplace Extensions add further capabilities to its products, such as text mining, web crawling and integration.
    • Ease of use: Given the complexity and sophistication of data science endeavors, data scientists value ease of use highly, as it enables them to be more productive. RapidMiner delivers this value by providing an intuitive interface, easy access to data sources, simple programming for developing models and easily understood results. Reference customers reported a short learning curve.
    *Gartner Magic Quadrant for Data Science and Machine-Learning Platforms, Carlie J. Idoine, Erick Brethenoux, Jim Hare, Peter Krensky, Nigel Shen, Svetlana Sicular, Shubhangi Vashisth, February 22, 2018.
    *This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from RapidMiner.
    *Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.