Monitoring and Maintaining Deployed Data Driven Models in Manufacturing (Pilot)

SUMMARY

What happens to your manufacturing model after it’s deployed? How do you ensure it continues to deliver value in a dynamic, real-world environment?

This lesson provides a high-level overview of the critical practices for monitoring and maintaining deployed machine learning models in manufacturing settings. It explores concepts such as data drift, model degradation, and the need for continuous evaluation to ensure models remain effective over time.

Designed for manufacturing professionals and data practitioners, this lesson empowers learners to bridge the gap between deployment and sustained success. Through engaging lessons and real-world examples, you’ll discover how monitoring, retraining, and proactive issue resolution are essential for achieving long-term reliability and alignment with business goals.

Whether you’re responsible for maintaining advanced predictive models or just starting to explore post-deployment strategies, this lesson equips you with the knowledge to keep your AI-powered solutions robust and impactful.

About The Author

Dilek Dustegor is a Professor of Computing Science at the University of Groningen in the Netherlands. She is interested in bridging the gaps between research, development and implementation using AI and automation. She is pursuing research about modeling, design and analysis of large scale / networked systems using IoT and ML techniques, with a special interest in smart city applications. She is a seasoned educator, and loves using the newest educational technologies for an enhanced learning experience.