Definition
Maintaining ML Systems refers to the ongoing processes required to ensure machine learning models perform reliably, securely, and ethically after deployment. This involves continuous monitoring of model performance, data drift detection, retraining models with new data, and updating infrastructure to prevent degradation or security vulnerabilities. It matters because poorly maintained ML systems can lead to inaccurate predictions, biased outcomes, and significant operational risks. For instance, a fraud detection model that isn't retrained on new fraud patterns might start missing fraudulent transactions. This differs from initial model development, which focuses on building the model, whereas maintenance focuses on its long-term operational health. Concepts related to maintaining ML systems are increasingly appearing on advanced AI and cybersecurity certification exams.