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The world is experiencing the perfect Artificial Intelligence/Machine Learning storm of continuous research and innovation, accessibility to current and historical data, and the ever-decreasing price of camera and sensor components. Leading companies have harnessed this to create new platform businesses or invent life-saving medical devices and security systems. But AI and ML aren’t for every original equipment manufacturer (OEM), and they shouldn’t be. 

Intelligent analytics, artificial intelligence, and machine learning have become buzzwords that are used interchangeably by media, consumers, and brands alike. But to an engineer, they are very different. If an OEM goes down the machine learning path when their product doesn’t warrant it, development will be much more expensive, and the final product will also cost more due to the increase in computer power required. Our advice? There is a time and place for machine learning. Don’t develop around it if you don’t need it.

In this free white paper download, we put machine learning into context using real-life applications to help OEMs determine if their product could benefit from it. Then, we walk through the five critical steps that will set a machine learning development project up for success.

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