Summary
In this chapter, we have learned about different ideas related to concept drift. These can be applied to both streaming (batch streams) and live data as well as trained ML models. We also learned how both statistical and contextual methods play an important role in estimating model metrics by determining model drift. The chapter also answered some important questions related to model drift and explainability and helped you to understand model calibration. In the context of calibration, we also learned about fairness and calibration and the limitations of achieving both at the same time.
In the next chapter, we will learn more about model evaluation techniques and handling uncertainties in model-building pipelines.