JULY 2020CIOAPPLICATIONS.COM8In My ViewMarkus Levy is the director of Machine learning technologies at NXP, where he is responsible for driving the deployment strategy for machine learning technologiesas well as the AI and machine learningroadmap within the NXP Edge Processing business line. Being an entrepreneurial executive with a global perspective on the embedded industry, Levy has more than 25 years of experience in the microprocessor, microcontroller, SoC, and embedded system marketing, analysis, and business development.Major challenges affecting the Machine Learning spaceI've been working on machine learning technology for the last couple of years. If you look back even one or two years, many people were already interested in this technology. Rightly so, since machine learning shows a lot of promise for the future; however,many people don't know how to get started. It's easy to look at this technology and appreciate the potential value it can deliver. Still, a common challenge many people face is understanding how they can add machine learning capability to a traditional embedded product they are building. Figuring out what type of machine learning capability to be added starts with understanding the intended application. A significant foundation related to machine learning comes from the ability to collect data that you would use to train a model. And once you have the data, then there are open source and proprietary tools available to carry out model training and deployment on the hardware at the endpoints. Trends Shaping the IndustryOn the proprietary side when it comes to tools, there are a growing number of companies providing a wide range of capabilities, some of which become our ecosystem partners. For example, one type of partner makes tools that help customers collect and label their data and then use itfor training their models. This type of technology is challenging to build up internally, especially if you're a small company. On the open-source side, there is a tremendous amount of activity from companies such as Google and Facebook, who provide or sponsor the TensorFlow and PyTorch training frameworks, respectively. Ancillary TensorFlow applications exist as well, such as TensorFlow Litefor mobile phones or embedded devices and TensorFlow Micro for deploying even smaller versions on other types of edge devices.Another trend is that many third-party companies are developing specialized applications for machine learning. For instance, some companies are experts at gesture recognition. A good example of this is using specific gestures to raise or lower the volume or to changetracks--instead of pushing buttons to navigate your car's entertainment system. Some companies, such as Arcturus Networks, are building software modules for surveillance cameras and then sellingthemto camera manufacturers for integration into their end products. These are just a few examples of the types of companies that are popping up with specialties related to application functions. Machine Learning: The Future from the Perspective of Model BuildingMARKUS LEVY, DIRECTOR OF ENABLING TECHNOLOGY, NXP SEMICONDUCTORSMarkus Levy
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