What metrics would you track to evaluate the performance of your ML pipeline?

Focus on identifying key metrics that reflect both the effectiveness and efficiency of the ML pipeline. Discuss performance metrics such as accuracy, precision, recall, and F1 score to evaluate model quality, as well as operational metrics like latency, throughput, and resource utilization to assess pipeline efficiency. Consider the importance of tracking data quality metrics, such as data drift and feature importance, to ensure that the model remains relevant over time. Be prepared to explain how you would monitor these metrics in real-time and the implications of each metric on the overall product strategy.