Health KPIs of AI/ML models in production
summary
Describes key production monitoring metrics for deployed AI/ML systems, focusing on how models interact with real-world, messy data streams and why traditional software/infrastructure KPIs are insufficient. Presents specific health KPIs to track after launching models to detect data issues and prevent Garbage-In-Garbage-Out decisions.
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chapters (8)
- 0:00Start
- 2:16LETS STOP DEPLOYING ML TO PRODUCTION
- 2:46KEEP EXPLORING!
- 3:01In Production, time is a factor
- 3:31Concept Drifts
- 4:16Model Probability != Model Confidence
- 4:46BUSINESS
- 5:01AI PERFORMANCE MANAGEMENT
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