Most machine learning classes focus on the learning algorithms, but largely ignore larger system issues. Implementation Considerations for Machine Learning at the Edge of the Cloud examines the broader set of problems that have to be dealt with before any embedded implementation can be brought to market. These range from hardware choices (MCU vs MPU, communications media, sensors, board implementation, etc), packaging issues, machine learning library choices, data collection, feature engineering, cloud interfaces, security and more. All of the above are discussed with the benefit of hindsight from an engineer tasked with putting together an end-to-end design system for embedded machine learning while working for a major semiconductor manufacturer.