Model Monitoring
Model monitoring is the practice of continuously tracking the performance, behavior, and health of deployed machine learning models in production environments. It involves collecting metrics on predictions, data drift, model drift, and system performance to detect issues like degradation, bias, or failures. This ensures models remain accurate, reliable, and fair over time as real-world data and conditions evolve.
Developers should learn and use model monitoring when deploying machine learning models to production, as it helps maintain model effectiveness and trustworthiness. It is critical for applications in finance, healthcare, or e-commerce where model failures can lead to significant financial loss, safety risks, or poor user experiences. Use cases include detecting data drift in recommendation systems, monitoring for bias in hiring algorithms, and ensuring uptime in fraud detection models.