What is the main reason to monitor a deployed Generative AI model?

Monitoring a deployed Generative AI model is essential primarily to detect performance degradation or drift over time. As models operate in real-world scenarios, their performance can change due to various factors such as shifts in input data distributions, evolving user preferences, or external changes in the environment. This phenomenon, known as model drift, can lead to declining accuracy and effectiveness, making it vital to continuously track performance metrics.

By monitoring the model, practitioners can identify when its output deviates from expected behavior or when it becomes less effective in meeting the goals for which it was designed. Timely detection of these issues allows for corrective actions to be implemented, such as retraining the model with updated data, adjusting its parameters, or even deploying entirely new models, ensuring that it continues to deliver value and remains aligned with user needs and expectations. In contrast, options related to adherence to pre-set training data, reducing model complexity, or preventing updates do not address the ongoing operational challenges that arise after the model has been deployed.