# A Multi-Criteria Automated MLOps Pipeline for Cost-Effective Cloud-Based Classifier Retraining in Response to Data Distribution Shifts

**Authors:** Emmanuel K. Katalay, David O. Dimandja, Jordan F. Masakuna  
**arXiv:** [2512.11541](https://arxiv.org/abs/2512.11541)  
**Date:** 2025-12-12  
**Summary page:** https://www.summarizepaper.com/en/paper/2512.11541v1/

## Key points

- The paper introduces a Multi-Criteria Automated MLOps Pipeline for Cost-Effective Cloud-Based Classifier Retraining in Response to Data Distribution Shifts.
- ML models' performance deteriorates when data distribution changes, requiring model retraining and redeployment.
- Traditional retraining methods are manual, needing human intervention to trigger updates.
- The proposed pipeline uses algorithms to monitor and detect shifts in data distributions for automated model updates only when significant changes occur.
- By focusing on relevant distribution shifts, unnecessary retraining cycles are minimized, reducing computational overhead and optimizing resource utilization.
- The approach is beneficial in dynamic settings where data distribution changes are common.
- Experiments on benchmark datasets show significant improvements in model accuracy and robustness compared to conventional retraining strategies.
- Automation of the retraining process provides cost-effective solutions for maintaining ML models in cloud-based environments while reducing operational costs.
- The work contributes to advancing efficient ML operations in response to evolving data distributions, enhancing the reliability and adaptability of machine learning systems.

## Summary

In their paper, "A Multi-Criteria Automated MLOps Pipeline for Cost-Effective Cloud-Based Classifier Retraining in Response to Data Distribution Shifts," Emmanuel K. Katalay, David O. Dimandja, and Jordan F. Masakuna introduce an  designed to address the challenge of  in machine learning models. They highlight that the performance of ML models often deteriorates when the underlying data distribution changes over time, necessitating model retraining and redeployment. Traditional  are manual, requiring human intervention to trigger these updates. The authors' proposed pipeline utilizes  to monitor and detect shifts in data distributions, enabling automated model updates only when significant changes occur. By focusing on relevant distribution shifts, the pipeline minimizes unnecessary retraining cycles, reducing computational overhead and optimizing resource utilization. This approach is particularly beneficial in dynamic real-world settings where data distribution changes are common. The study showcases the effectiveness of their framework through experiments conducted on various benchmark anomaly detection datasets. Results demonstrate significant improvements in model accuracy and robustness compared to conventional retraining strategies. By automating the retraining process and emphasizing cost-effective solutions for maintaining ML models, the authors provide a foundation for deploying more  in cloud-based environments while mitigating operational costs associated with frequent model updates. Their work contributes to advancing efficient  in response to evolving data distributions, ultimately enhancing the reliability and adaptability of machine learning systems in dynamic settings.