What is the purpose of monitoring deployed models?

Monitoring deployed models serves several key functions, primarily focused on managing their performance over time. The purpose of this monitoring is to encompass continuous assessment of the model's effectiveness, which includes identifying when the model may need retraining due to changing data patterns or other factors that might impact its efficacy.

By actively managing retraining risks, practitioners can ensure that the models remain aligned with current conditions and user needs. This is particularly important in fields like dentistry, where new data or techniques can emerge and impact how AI-driven tools function. Effective monitoring helps in making informed decisions about when to update the model, thereby safeguarding its accuracy and reliability in real-world applications.

The other options reflect different aspects of AI and model management but do not encapsulate the comprehensive intent of monitoring deployed models. For example, evaluating performance without adjustments does not consider the need for ongoing optimization. Assessing user dissatisfaction may provide insights but does not directly address model performance; and limiting ongoing improvements contradicts the very goal of continuously enhancing model effectiveness in response to new challenges and information.