Why Your Data Teams Fail at Edge Computing

Edge computing promises low latency and privacy, but many data teams are hitting hard walls when trying to deploy machine learning models directly on devices. In this episode, Lucas and Luna break down the specific engineering trade-offs that cause edge AI projects to stall. We look at a real-world case where a major logistics company attempted to run computer vision models on warehouse robots, only to face catastrophic battery drain and inconsistent inference speeds. The conversation covers model quantization, hardware constraints, and why sending raw data to the cloud is sometimes the smarter business decision. If you are building IoT systems or autonomous agents, this episode explains how to avoid the most common pitfalls in distributed AI architecture. #FexingoBusiness #BusinessPodcast #EdgeComputing #MachineLearning #AIEngineering #DataScience #IoT #ModelQuantization #Latency #BatteryLife #ComputerVision #LogisticsTech #DistributedSystems #CloudVsEdge #Robotics #TechStrategy #InferenceCosts #DataArchitecture Keep every episode free: buymeacoffee.com/fexingo