This is a guest post from Paul Pilotte, technical marketing manager for data science and predictive analytics.Gartner recognizes MathWorks as a Visionary in its January 2019 Magic Quadrant for Data Science and Machine Learning PlatformsDeep learning and AI are top of mind in many organizations we work with at MathWorks. It’s 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.
If you’ve followed this blog, you’ve 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.AI 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.
Gartner 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.If you’re 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.We believe the Gartner recognition is a testament to this, and we’re honored Gartner named MathWorks a Visionary in the 2019 Magic Quadrant for Data Science and Machine Learning Platforms. There’s 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.This is only the beginning. We remain focused as ever in making AI with MATLAB an easy, enjoyable, and productive experience.To learn more about why we believe we were positioned as a Visionary by Gartner, check out these pages:
Disclaimer: 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.
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I saw the Gartner magic square post in a separate article. I am thrilled MATLAB is on there. It helps justify my push for MATLAB and MathWorks products within my own organization. It also point out the issue with things like Anaconda by comparison.
I saw the article here:
There are a couple things I would point out:
1) MATLAB could help with the automation of some of the complexities of their products that require an extreme amount of labor in my opinion. For example, outside of running the regressionLearner or classificationLearner tools (which produce models that are far from optimal), and assuming the model type from one of those GUIs is the right solution to optimize, I would otherwise have to manually explore all of these: 1) fitrtree, 2) fitrsvm, fitrensemble, 3) fitrm, 4) fitgmle, 5) fitlme, 6) fitglm, 7) fitlm, 8) fitlm - JUST TO NAME A FEW - or 1) fitctree, 2)fitcdisc, 3)fitcknn, 4) fitcnb, 5)fitcsvm, 6) fitcecoc, 7) fitceensemble, and so on. Even the optimization search for each of these requires a lot of tuning.
2) The MathWorks website for documentation is incredibly fragmented. I have a Rolodex and notepads covered in MATLAB notes from things I run across that should be consolidated. It's akin to any group at MathWorks creating a new branch of their website with documentation and blogs that are not centralized, but appears to be hodge-podge.
3) Why isn't XGBoost in MATLAB? I keep reading articles about how this tool keeps winning data science competitions. It is also the primary tool used by CERN for analysis of their Large Hadron Collider data - and it can be parallelized.
Anyway, congratulations on the accomplishment. I'd be glad to make further suggestions. I would like to see MATLAB and MathWorks in the top right square far and above every other platform!
Chris - this is the kind of feedback we love to hear. Thanks so much for taking the time. Paul will be in touch so his team can hear more about your thoughts.