Monitoring and Maintaining Machine Learning Systems

Effective monitoring and maintenance of machine learning (ML) systems is a crucial component of ensuring their reliability, robustness, and relevance over time. Given the dynamic nature of ML models and the environments in which they operate, ongoing oversight is necessary to detect and address issues such as model drift, bias, and performance degradation. This chapter explores the multifaceted processes involved in monitoring and maintaining ML systems from a data governance perspective. We will discuss the policies, best practices, challenges, and tools required to sustain ML models in production, ensuring that they continue to function effectively and ethically over time.