In a talk at the Bristol MLOps IRL Meetup, Thorben Louw discussed model drift, the deterioration in the performance of machine learning models over time. Louw, representing Equal Experts, a software consultancy, explained that model drift can be categorized as data drift or concept drift. Data drift refers to changes in input variables or labels, while concept drift refers to changes in the relationship between variables. To combat model drift, Louw recommended monitoring data quality, utilizing statistical tools to detect drift, and updating data sets for future training. These approaches can help maintain model performance and address the challenges posed by model drift.
