Model monitoring refers to continuously checking the performance of AI models after deployment. During model monitoring, various metrics are tracked, such as accuracy, response time, and the rate of errors. This helps detect any issues that may arise, such as changes in the underlying data or user behavior. If the model starts to underperform, monitoring tools can alert data scientists or engineers, allowing them to take corrective action.
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