There is widespread interest in deploying artificial intelligence (AI) and machine learning (ML) applications in industrial environments to increase productivity and efficiency while also achieving savings in operating costs. However, as any engineer or engineering manager will tell you, there are three major issues that need to be addressed with regard to adding "smarts" to an installed base of “dumb” machines, from motors to HVAC systems.
First, there aren’t enough people with AI and ML expertise to satisfy the demand, and what experts are available don’t come cheap. Second, there is a lack of qualified data sets with which to train the AI and ML systems, and any data sets that are available are jealously guarded. Third, AI and ML systems have traditionally demanded high-end processing platforms on which to run.
What is required is a way to enable existing engineers and developers without AI and ML experience to quickly create AI and ML systems and deploy them on efficient, low-cost microcontroller platforms. An interesting startup called Cartesiam.AI is addressing all these issues with its NanoEdge AI Studio. Let me explain how.