What triggers the need for retraining a machine learning model based on metrics?

Retraining a machine learning model becomes necessary when there is a performance degradation or evidence of data drift. Performance degradation indicates that the model is no longer making accurate predictions, which can happen as the underlying data distribution changes over time or as new trends emerge that weren't present during the initial training phase. Data drift specifically refers to the changes in the statistical properties of the input data, which can impact the model's performance if it was built on earlier data that no longer represents the current reality. Therefore, monitoring performance metrics regularly helps identify these issues, allowing practitioners to decide when to retrain the model to ensure it remains accurate and effective.

Changes in user interface, increases in hardware capabilities, or the implementation of new data sources do not directly indicate that the model itself needs to be retrained. Each of those factors may indeed influence how a model is integrated or utilized, but they do not inherently trigger a need for retraining the model based on its predictive performance.