Over the last few years, we're starting to see machine learning be more effectively deployed closer to where data is collected in embedded systems. These end point devices may be resource constrained though, either in terms of power, memory or communication capabilities - sometimes all three. Being able to apply machine learning on these end point devices is possible, and enables system-wide efficiencies to be realized. This talk will explore the requirements and tradeoffs for such systems to be considered when using the Zephyr RTOS and Tensorflow Lite for Embedded Microcontrollers projects.