Continuously tracking AI model performance, data quality, and system health in production to detect issues early. Like having health checkups to catch problems before they become serious.
Model monitoring alerts the team when prediction accuracy drops below 95% or when incoming data looks different from training data.
Continuously tracking AI model performance, data quality, and system health in production to detect issues early. Like having health checkups to catch problems before they become serious.
Model monitoring alerts the team when prediction accuracy drops below 95% or when incoming data looks different from training data.
Related concepts include MLOps, Model Drift, Data Drift. Understanding these connections helps build a comprehensive knowledge of cloud computing concepts.