Overview
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
Introduction to the Talk
Watch the introduction video before the talk:
Event Photos
Connect with the Speaker
Megan Robertson
Learn more about Megan and her work in data science and machine learning.
Connect with Megan