Managing Drift Effectively in a Dynamic Environment
Drift is a significant factor in the performance deterioration of deployed deep learning models over time, encompassing concept drift, data drift, and model drift. Let’s understand the drift of a deployed model through a culinary-based analogy. Imagine a deployed deep learning model as a skilled chef who aims to create dishes that delight customers but excels in a particular cuisine. Concept drift occurs when the taste preferences of the diner shift, which alters the relationships between ingredients and popular dishes that can satisfy the diner’s palate. Data drift, on the other hand, happens when the ingredients themselves change, such as variations in flavor or availability. Finally, model metric monitoring alerts happen most straightforwardly when the chef loses customers. In all cases, the chef must adapt their dishes to maintain their success, just as deep learning models need to be updated to account for concept...
