Why Your Data Models Fail at Scale
transcript
show notes
Most data teams build models that work perfectly in the lab but collapse under real-world traffic. In this episode, we explore how Microsoft tackled massive latency issues by moving from batch processing to online learning systems. We break down the specific trade-offs between model freshness and computational cost, and why the ten percent improvement in response time mattered more than any accuracy metric. Lucas and Luna dissect the engineering decisions behind scalable AI infrastructure.
#DataScience #MachineLearning #ScalableAI #Microsoft #OnlineLearning #ModelDeployment #LatencyOptimization #RealTimeAnalytics #FeatureStore #ModelFreshness #Infrastructure #TechEngineering #FexingoBusiness #BusinessPodcast #DataDriven #AIOps #ComputationalCost #ProductionReady
