Error analysis x Model monitoring: how are they different?

Machine learning (ML) development involves complex challenges, requiring effective coordination among teams to avoid inefficient use of resources. A crucial part of this process includes error analysis and model monitoring, two distinct yet complementary stages that significantly impact the quality of the models deployed. Model monitoring serves as the last line of defense, ensuring that models remain effective in changing environments by tracking performance and identifying issues like data drifts. However, relying solely on monitoring can lead to inefficiencies, as demonstrated by Zillow's costly mistake with its iBuyer model, which highlighted the risks of over-reliance on automated systems without thorough error analysis. Error analysis involves understanding when and why models fail, offering insights that help improve model robustness and prevent issues before deployment. By systematically conducting error analysis, organizations can proactively identify potential problems, saving resources and ensuring model reliability. Despite their shared goal of delivering high-quality models, these stages require distinct activities, underscoring the importance of both in a robust ML development pipeline.