Learn how to effectively deploy and manage machine learning models across large fleets of single board computers (SBCs) in this comprehensive technical talk. Explore various architectures for running ML models on devices like Raspberry Pi and Jetson Nano, while understanding the key benefits and challenges of edge computing implementation. Discover practical approaches to reduce latency, enhance privacy, and optimize bandwidth usage in edge ML applications. Through multiple demonstrations, examine real-world implementations of these architectures in NLP, computer vision, and sensor data monitoring applications. Gain valuable insights into scaling ML-powered solutions across distributed edge devices and learn best practices for fleet management from industry expert Seth Clark, Co-founder & Head of Product at Modzy.