The Ultimate Guide to
Machine Learning for Embedded Systems
Machine learning is a powerful method for building models that use data to make predictions. In embedded systems — typically running microcontrollers and constrained by processing cycles, memory, size, weight, power consumption, and cost — machine learning can be difficult to implement, as these environments cannot usually make use of the same tools that work in cloud server environments.
This Ultimate Guide to Machine Learning for Embedded Systems includes information on how to make machine learning work in microcontroller and other constrained environments when the data being monitored comes from sensors.