Why Data Teams Fail at Edge Computing
transcript
show notes
Most data teams treat edge computing as a hardware problem, but it is actually a software latency and synchronization challenge. In this episode, we look at why deploying machine learning models to thousands of remote sensors fails when teams ignore the cost of data round-trips. We examine the specific case of predictive maintenance in industrial IoT, where the gap between model accuracy and real-world inference speed creates silent failures. Lucas and Luna break down the architectural trade-offs between local inference and cloud training, and how to design systems that work when connectivity drops.
#EdgeComputing #MachineLearningOps #IndustrialIoT #DataArchitecture #PredictiveMaintenance #LatencyOptimization #ModelDeployment #FexingoBusiness #BusinessPodcast #TechStrategy #DataScience #CloudVsEdge #RealTimeAnalytics #SystemDesign #DataEngineering #OperationalTechnology #DigitalTransformation #Fexingo
