Adaptive Mechanisms for Model Retraining in Production: Drift Triggers, Retraining Prioritisation, and Reduced Data Preparation Time through Automated Orchestration

Citation: Andrei Shcherbinin, "Adaptive Mechanisms for Model Retraining in Production: Drift Triggers, Retraining Prioritisation, and Reduced Data Preparation Time through Automated Orchestration", Universal Library of Innovative Research and Studies, Volume 03, Issue 01.

Copyright: This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This work provides an analytical and theoretical–practical justification for the effectiveness of deploying adaptive retraining mechanisms for machine learning models in unstable industrial environments that evolve dynamically over time. The relevance of the study is driven both by the sharp increase in investment in artificial intelligence in 2025 and by the objective need to curb losses in model accuracy caused by concept drift and input data drift. The study adopts a comprehensive methodological approach that combines a systematic review of scientific publications, a comparative analysis of drift-detection algorithms, and a case study component. The results indicate that introducing automated process orchestration using Apache Airflow, together with monitoring systems that provide full model coverage, reduces incident detection time by 60% and lowers the labour intensity of data preparation for subsequent retraining by 40%. An original hybrid lifecycle governance model for ML systems is proposed, combining statistical activation mechanisms with business-oriented principles to prioritise computational resources. The conclusions empirically support the hypothesis that a meaningful increase in model accuracy—up to 11% in marketing attribution tasks—can be achieved while simultaneously reducing operating costs. The study’s materials carry both practical and scientific value for machine learning practitioners, data architects, and digital unit leaders responsible for scaling and ensuring the resilient operation of AI solutions in production environments.

Keywords: Machine Learning, MLOps, Concept Drift, Retraining Automation, Data Orchestration, Model Degradation, Real-Time Monitoring, Apache Airflow, Retraining Prioritisation, Operational Efficiency.

https://doi.org/10.70315/uloap.ulirs.2026.0301013