How should monitoring be handled for deployed AI models?

Managing retraining risks is crucial for the effective monitoring of deployed AI models. This approach recognizes that although models can perform well initially, their effectiveness may diminish over time due to changes in data patterns, user behaviors, or external conditions. It involves continuously assessing the model's performance and the data on which it was trained, while also implementing strategies to mitigate risks associated with retraining, such as data drift, model bias, and overfitting.

By managing these risks, practitioners can ensure that the AI model remains relevant and accurate over time. This process may involve regular evaluations using validation datasets, implementing automated monitoring systems to detect performance issues, and employing best practices in model retraining based on the evolving understanding of the data landscape.

Other choices do not align with best practices for AI monitoring; ignoring feedback or only updating models in response to major issues can lead to significant drops in performance. Moreover, manually retraining every model monthly may not be practical or necessary, leading to excessive resource utilization without a proportional benefit.