Advanced Techniques for Mitigating Model Drift and Enhancing the Robustness of AI/ML Models

Abstract

The implementation of AI/ML models in mission-critical applications requires rigorous drift mitigation as well as model robustness enhancement. This paper studies the state-of-the-art approaches to these problems which involve the inclusion of continuous monitoring and drift detection, data management strategies, model retraining and updating, adversarial training for robustness and stability, enhancing model interpretability and governance frameworks, and robust deployment strategies and frameworks. Continuous process control monitored through the use of statistical process control and concept drift detection algorithms enables the early detection of performance deterioration. Implementing data quality assurance, feature engineering, and augmentation processes ensures that training data exists in its representative form. The incremental learning, transfer learning and online learning used for model retraining will help with adapting to new data distributions. Adversarial training that includes gradient-based attacks and generative adversarial networks enhances resistance against changes through perturbations. Employing the above methods the organizations would be able to prevent machine drift, strengthen robustness, and secure the robust performance of machine intelligence.