AI Monitoring
AI Monitoring refers to tools and practices for tracking the performance, behavior, and health of artificial intelligence and machine learning models in production environments. It involves collecting metrics on model predictions, data drift, system resources, and business outcomes to ensure reliability, fairness, and efficiency. These tools help detect issues like model degradation, bias, or infrastructure failures that could impact AI-driven applications.
Developers should learn AI Monitoring when deploying machine learning models to production, as it is critical for maintaining model accuracy and trustworthiness over time. It is essential for use cases like fraud detection, recommendation systems, or autonomous vehicles, where real-time performance monitoring can prevent costly errors or ethical issues. By implementing AI Monitoring, teams can automate alerts for anomalies, comply with regulatory standards, and optimize model retraining cycles.