Summary
In this chapter, we extensively explored the significance of monitoring both models and data, emphasizing the crucial role of drift detection. Our understanding deepened as we delved into the spectrum of statistical tests at our disposal, which are adept at identifying diverse forms of drift encompassing numerical and categorical features.
Moreover, we engaged in a comprehensive walk-through, exemplifying the application of these concepts. Through a simulated model drift scenario using a synthetic e-commerce dataset, we harnessed the power of various statistical tests from the scipy.stats package to accurately pinpoint instances of drift.
As we venture into the next chapter, our focus will pivot toward elucidating the organization within the Databricks workspace and delving into the realm of continuous integration/continuous deployment (CI/CD).
