Artificial Intelligence

Apply machine learning and deep learning

MathWorks Is a Leader in the Gartner Magic Quadrant for Data Science and Machine Learning Platforms 2020

This is a guest post from Paul Pilotte, technical marketing manager for AI, data science and deep learning.
We’re pleased to have been named a "Leader" by Gartner this year, with the furthest placement for "completeness of vision" of all the Leaders in the quadrant. Last year, Gartner recognized MathWorks as a "Visionary", so in our opinion this move into the Leaders quadrant shows strong progress.
I wanted to share this news and talk about how, as a key player in the AI landscape, MathWorks thinks differently about AI.
AI continues to be a strategic priority for many companies.  We certainly see this in the growing number of complex projects we’ve helped our customers complete in the past year, and we expect this trend to continue.  See these pages for examples of how customers are using MATLAB for deep learning and machine learning.
We’re seeing AI projects expand to include applications across image processing, computer vision, signal and audio processing, robotics, and control systems. The engineering and operations teams we work with are using MATLAB to incorporate AI across the entire system design workflow. Success in developing AI-enabled systems requires more than training an AI model: You also need high-quality data, engineers and scientists focused on AI, and an understanding of the complete AI workflow.
We believe our recognition as a Leader in Gartner's 2020 Magic Quadrant for Data Science and Machine Learning demonstrates our ability to help organizations:
  • Equip teams with limited AI or data science skills as well as those with advanced skills to apply AI successfully
  • Incorporate AI across the complete system design workflow including data preparation, analytics and modeling, simulation and test, and production
  • Deploy AI models on embedded devices, edge, enterprise systems, and the cloud
  • Use Simulink to tackle system design, integration, and validation challenges when developing AI-driven systems
We continue to invest in creating tools and solutions to make it easy to design and build models, train and visualize networks, and deploy models to more hardware targets as well as enterprise systems and cloud.  Here’s a sample of what’s new in MATLAB for deep learning:
  • Automate labeling data including images, videos, signals, and audio recordings
  • Graphically design and analyze deep learning using the Deep Network Designer app
  • Build advanced network architectures like GANs, Siamese networks, attention networks, and variational autoencoders
  • Import and export models with other deep learning frameworks (such as TensorFlow and PyTorch) using the ONNX model format
  • Train reinforcement learning policies to implement controllers and decision-making algorithms for complex systems such as robots and autonomous systems
  • Automate deployment of inference models to CPUs, GPUs, and FPGAs.
See the Latest Features for more and keep an eye out for our upcoming R2020a release.
There’s much more to come. We’ll continue to release new features in our products, focused on making MATLAB an easy and productive environment for AI.  
To learn more check out these pages:
Gartner Magic Quadrant for Data Science and Machine Learning Platforms, Peter Krensky, Erick Brethenoux, Jim Hare, Carlie Idoine, Alexander Linden, Svetlana Sicular, 12 February 2020. 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 MathWorks.
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, express or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.
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