SAS is a Leader in 2019 Gartner Magic Quadrant for Data Science & Machine Learning Platforms
For the sixth consecutive year, SAS is positioned as a Leader.
“Machine learning is a critical tool in the modern data scientist toolbox,” said Lorry Hardt, Artificial Intelligence and Machine Learning Strategist at SAS. “It allows organizations to quickly identify opportunities for their business, but also avoid risks that may go unnoticed by humans.”
SAS’ evaluation is based on two solutions key to the success of its data scientist users – SAS Visual Data Mining and Machine Learning and SAS® Enterprise Miner™. Both offer users the ability to solve complex analytical problems that drive better decision making.
SAS Visual Data Mining and Machine Learning, which runs on the SAS® Viya® engine, includes the latest statistical, machine learning, deep learning and text analysis algorithms that accelerate structured and unstructured data explorations, while also supporting popular open source languages. It unifies the entire machine learning process, from data access/transformation and preparation to scoring, in one environment.
SAS Enterprise Miner helps identify relationships and patterns buried in a company’s data. It streamlines the data mining process to create accurate predictive and descriptive analytical models to find the best fit, no matter the size of the data set.
Within the field of data science, Machine Learning (ML) is the most popular area. It is one that warrants specific attention by those evaluating these platforms.
Gartner**
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*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 SAS. 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.
**Gartner Magic Quadrant for Data Science & Machine Learning by Carlie Idoine, Peter Krensky, Erick Brethenoux and Alexander Linden, 28 January 2019.
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