How Data Drift and Concept Drift Impact Retraining Strategies
In the world of MLOps, the tale of Company A and Company B is a familiar one. Company A set out on a grand machine learning journey, only to be met with unexpected challenges due to data drift and concept drift. These two factors significantly affect a model’s performance over time, making the choice of the right retraining strategy crucial.
Data drift, also known as covariate shift, occurs when the statistical properties of input data change. Imagine a child whose multiplication tables are limited to a certain range; when asked a question outside that range, they may provide inaccurate answers. This is analogous to how a model trained on specific data may struggle when faced with new or changing data demographics.
On the other hand, concept drift happens when the relationship between input data and the target variable changes. For example, as language evolves, the spelling of words may change, leading to