| ABSTRACT |
In today’s dynamic digital ecosystems, machine learning (ML) models face rapid data drift, evolving feature relationships, and shifting operational environments. Conventional static training pipelines—where models are trained periodically and redeployed manually—struggle to maintain predictive relevance and responsiveness under such continuous change. This paper introduces an Adaptive Model Training Pipeline (AMTP) framework that embeds real-time feedback loops into every stage of the ML lifecycle, enabling self-evolving behavior without constant human supervision. The proposed system continuously monitors model performance metrics, detects concept drift, and triggers on-demand retraining through automated orchestration layers. A closed-loop feedback mechanism—comprising performance telemetry, reinforcement-based feedback, and explainability-informed adjustments—ensures that deployed models remain contextually accurate and resilient. Experimental analysis demonstrates how adaptive pipelines can reduce model degradation time by up to 60% while maintaining consistent inference quality in volatile data environments. This research highlights the convergence of MLOps automation, online learning, and adaptive control theory in creating next-generation self-correcting AI systems designed for scalability and reliability in production. |
