Data drift refers to the change in input data for a predictive model over time which leads to a decrease in the model's performance. This drift is a natural phenomenon in most real-world data because the environment in which these models operate changes all the time. Data drift is a critical aspect to monitor during the lifecycle of a machine learning model, as not accounting for it could lead to sub-optimal predictions or incorrect insights.
Data Drift in practice
Data drift occurs when the statistical properties of the target variable, which the model is trying to predict, change in the unobserved, incoming data. This often happens in dynamic environments where data can change rapidly and unpredictably.
In order to monitor and address data drift, you need to version not only your models but also your data. This means keeping track of which model is trained on which data. Also, keeping track of model accuracy over time will typically show a decrease in performance, indicating that data drift may be occurring.
Moreover, implementing automatic model retraining, alerting, and health checks as part of your machine learning pipeline can help to ensure that models remain accurate even when data drift occurs.
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