{"id":1164,"date":"2019-02-06T20:32:49","date_gmt":"2019-02-06T20:32:49","guid":{"rendered":"https:\/\/blogs.mathworks.com\/deep-learning\/?p=1164"},"modified":"2019-02-11T15:49:45","modified_gmt":"2019-02-11T15:49:45","slug":"gartner-magic-quadrant","status":"publish","type":"post","link":"https:\/\/blogs.mathworks.com\/deep-learning\/2019\/02\/06\/gartner-magic-quadrant\/","title":{"rendered":"Gartner Magic Quadrant"},"content":{"rendered":"This is a guest post from Paul Pilotte, technical marketing manager for data science and predictive analytics.<\/span>\r\n
<\/h6>\r\nGartner recognizes MathWorks as a Visionary in its January 2019 Magic Quadrant for Data Science and Machine Learning Platforms<\/span>\r\n
<\/h6>\r\nDeep learning and AI are top of mind in many organizations we work with at MathWorks. It\u2019s inspiring for us to see many engineers and scientists learning and applying deep learning in applications from UAVs using AI for object detection in satellite imagery to improved pathology diagnosis for early disease detection during cancer screenings.<\/span>\r\n
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<\/h6>\r\nIf you\u2019ve followed this blog, you\u2019ve seen how MATLAB offers a comprehensive deep learning workflow that simplifies and automates data synthesis, labeling, training, tuning, and deploying deep learning to AI-driven systems, including enterprise applications, embedded functionality, and edge systems. This makes AI accessible to engineers and scientists without previous data science experience. These tools are also broadening the applications of deep learning from image and computer vision to the many applications that use time-series data such as audio, speech, financial time series, and IoT time-stamped data.<\/span>\r\n
<\/h6>\r\nAI is also on the minds of executives. The office of the CEO and executive management are increasingly making it a strategic imperative to reap the promise of AI by sponsoring a data-centric culture across their organizations. <\/span><\/td>\r\n
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<\/h6>\r\nGartner has foreseen the strategic importance of AI and its research in this area is helping guide business leaders in this transformation to find new ways to improve speed and efficiency and unlock better outcomes for their customers.<\/span>\r\n
<\/h6>\r\nIf you\u2019re surprised to see MathWorks recognized by Gartner, you may not realize that for many years we have expanded our focus to make MATLAB a great tool for enterprise applications designed and managed by IT and OT groups, because we see them increasingly connected to engineering teams. We have made it easy to use MATLAB for data science and machine learning on Azure and AWS, for scaling with multiple instances as well as multi-GPU hardware, and for MATLAB-based applications integrated with enterprise systems. An example is our recent integration with NVIDIA GPU Cloud to enable deep learning training on DGX on-premise systems as well as multi-GPU instances on the cloud.<\/span>\r\n
<\/h6>\r\nWe believe the Gartner recognition is a testament to this, and we\u2019re honored Gartner named MathWorks a Visionary in the 2019 Magic Quadrant for Data Science and Machine Learning Platforms. There\u2019s enormous potential for AI to transform asset-centric industries like automotive, aeronautics, Oil & Gas, utilities, industrial machinery, and many more. Today we work closely with leaders in these industries on applications including computer vision, predictive maintenance, robotics, advanced controls, optimization, and many more.<\/span>\r\n
<\/h6>\r\nThis is only the beginning. We remain focused as ever in making AI with MATLAB an easy, enjoyable, and productive experience.<\/span>\r\n
<\/h6>\r\nTo learn more about why we believe we were positioned as a Visionary by Gartner, check out these pages:<\/span>\r\n
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