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SAS a machine learning Leader in 2019 Magic Quadrant

04 Feb 2019

Gartner has recognised SAS as a Leader in its 2019 Magic Quadrant for Data Science and Machine Learning Platforms. 

The report evaluated SAS for its completeness of vision and ability to execute and, for the sixth consecutive year, gave them the rank of Leader in this Magic Quadrant.

“Machine learning is a critical tool in the modern data scientist toolbox,” says SAS artificial intelligence and machine learning strategist Lorry Hardt. 

“It allows organisations 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, more rapid decision making.

Running on the SAS Viya engine, SAS Visual Data Mining and Machine Learning 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 and deploying, in one environment. 

SAS Visual Data Mining and Machine Learning “received excellent scores for user interface and data exploration and visualisation. It also received strong scores for data preparation and automation and augmentation,” adds the report.

Valuable across any industry, SAS Enterprise Miner works on any platform and with any data type to 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. 

The Gartner report defines a data science platform as “A cohesive software application that offers a mixture of basic building blocks essential for creating all kinds of data science solution, and for incorporating those solutions into business processes, surrounding infrastructure and products.”

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