Exploring the types of drift
Drift is like a shift in the way things work with data. It happens when the data changes, or the environment it comes from changes. This can sometimes happen suddenly or quickly, sometimes slowly, or even in a recurring pattern. When it comes to drift, it’s important to look at the big picture, not just a couple of odd blips. Drift isn’t about those rare anomalies or one or two odd predictions; it’s about changes that stick around, like a new pattern that stays. These persistent shifts can mess up your model permanently, making it way less useful. It’s like if your friend suddenly started speaking a different language occasionally, which could lead to one-off confusion but not really be a problem. But if they started speaking a different language all the time, it’d be a big problem.
Furthermore, drift can be categorized into three main types: data drift, concept drift, and model drift. While concept drift is related...