Keeping ML Models Healthy Monitoring Alerting and Retraining

Deploying an ML model is just the beginning! Discover why sustained performance in production demands constant vigilance and proactive strategies. This video is your ultimate guide to ensuring your machine learning models remain accurate, reliable, and valuable in the ever-changing real world.

Learn how to master the critical post-deployment phases of your ML models. We’ll explore the inevitability of performance degradation, dissecting its primary culprits: data drift and concept drift. You'll dive into essential monitoring practices, including selecting key performance metrics for various model types and employing statistical tests for robust drift detection. The video also covers setting up robust alerting systems, implementing strategic model retraining approaches (automated and event-triggered), and understanding the crucial roles of reproducibility, validation, and human oversight to maintain peak model health and deliver lasting business value.

Video Chapters:
00:00 1. Post-Deployment Reality
00:27 2. Performance Degradation
00:55 3. Data Drift vs. Concept Drift
01:22 4. Key Performance Metrics
01:50 5. Statistical Drift Detection
02:17 6. Monitoring Concept Drift
02:45 7. Robust Alerting Systems
03:12 8. System Architecture
03:40 9. Model Retraining Strategies
04:07 10. Automated Retraining
04:35 11. Event-Triggered Retraining
05:02 12. Reproducibility & Validation
05:30 13. Human Oversight
05:57 14. Sustaining Business Value

#MLOps #ModelMonitoring #MachineLearning #DataDrift #ConceptDrift

Видео Keeping ML Models Healthy Monitoring Alerting and Retraining канала AI Engineering Topics