Putting ML Models into Production and Keeping Them There: Examining Model Drift
AMLC of the Rockies - November 2025
Explore the challenges of deploying machine learning models to production environments and maintaining their performance over time. This session dives deep into model drift, monitoring strategies, and best practices for keeping your ML systems reliable and accurate.
Topics Covered:
- Understanding model drift and its impact
- Monitoring and detection strategies
- Retraining pipelines and automation
- Production ML best practices