Gurgen Hovakimyan - Beyond Static Models: Detection and Interpretability of Concept Drift

Gurgen Hovakimyan, Staff R&D Engineer at Synopsys, presents a talk on
“Beyond Static Models: Advancing the Detection and Interpretability of Concept Drift.”

In real-world machine learning systems, data is rarely static - it evolves, shifts, and drifts over time. This talk explores how concept drift can silently undermine model performance and how to build systems that not only detect these changes but also adapt in real time.

In this session, Gurgen dives into:
🔹 The real-world challenges of maintaining ML model reliability
🔹 Methods for detecting and interpreting concept drift
🔹 Practical strategies for building adaptive learning systems

This talk was recorded during the PyData Yerevan June 2025 Meetup held on June 19, 2025, at the American University of Armenia (AUA).
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