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Why Great ML Models Fail in Production Intro + Agenda
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Classroom Contents
Why Good Models Fail After Deployment
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- 1 Why Great ML Models Fail in Production Intro + Agenda
- 2 Failure Mode #1: Data Drift — What It Is, Examples, Detection & Fixes
- 3 Failure Mode #2: Concept Drift — When the World Changes the Rules
- 4 Failure Mode #3: Label Drift — Shifting Base Rates & Recalibration
- 5 Failure Mode #4: Feature Pipeline Degradation & Training-Serving Skew
- 6 Failure Mode #5: Feedback Loops & Bias Amplification
- 7 Building an ML Monitoring Dashboard: Performance, Data Health, System Health
- 8 Retraining Strategies: Time-Based vs Performance vs Drift vs Hybrid
- 9 Production ML Best Practices + Final Takeaways & Outro
