Click-through rate (CTR) models are critical for digital advertisers, with the average CTR in Google AdWords across all industries being 3.17% on the search network and 0.45% on the display network. However, machine learning systems can be impacted by various factors such as contextual relevance, user attributes, time of day or seasonal fluctuations, and data quality issues. To address these challenges, it's essential to implement best practices for ML monitoring and observability with CTR models, including tracking key metrics like log loss, precision recall AUC, and time series of predictions versus actuals. By identifying root causes of performance degradation and adaptingively retraining models, teams can ensure their CTR models stay relevant and effective in the ever-changing digital advertising landscape. Effective data quality assurance is also crucial to prevent "garbage in, garbage out" scenarios, where inaccurate or missing data can significantly impact model performance.