Episode
How MLOps Teams Are Using Model Monitoring to Prevent Silent Failures
- Podcast
- The Data Science Podcast with Fexingo: Analytics, Machine Learning, and Data-Driven Conversations
- Published
- Jun 8, 2026
- Duration seconds
- 523
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Summary
Episode 39 of The Data Science Podcast explores the growing discipline of model monitoring in production. Lucas and Luna discuss why many data science teams still treat monitoring as an afterthought, how silent failures erode business trust, and what tools like Evidently AI, WhyLabs, and custom dashboards are doing about it. They walk through a real example from a fintech lending platform where a model drifted undetected for weeks, costing the company over $2 million in bad loans. The conversation also covers the three key pillars of monitoring: data quality, model performance, and operational health. Lucas shares a practical checklist for teams getting started with monitoring today. If you are deploying models to production, this episode will save you from waking up to a 3 AM pager alert. #ModelMonitoring #MLOps #DataScience #MachineLearning #ProductionML #SilentFailures #ConceptDrift #DataQuality #MLPipeline #Fintech #EvidentlyAI #WhyLabs #MLObservability #DataDrift #ModelGovernance #FexingoBusiness #BusinessPodcast #Technology Keep every episode free: buymeacoffee.com/fexingo