Automating Model Retraining in DevOps Pipelines with MLOps

Addressing Model Drift and Data Evolution

Authors

  • Emily Johnson Senior Machine Learning Engineer, Tech Innovations, San Francisco, USA Author

Keywords:

model retraining, DevOps, MLOps, model drift, data evolution

Abstract

As machine learning (ML) systems become increasingly integrated into business processes, the challenges associated with maintaining model performance over time have gained prominence. One significant challenge is model drift, which refers to the degradation of model accuracy due to changes in data distributions. To ensure ongoing model relevance in dynamic environments, automating model retraining within DevOps pipelines has emerged as a critical area of focus. This paper explores the strategies and techniques for automating model retraining using MLOps practices. It discusses the importance of continuous monitoring, data versioning, and pipeline orchestration in addressing model drift and data evolution. By implementing these MLOps strategies, organizations can streamline the retraining process, reduce downtime, and enhance the overall effectiveness of their machine learning initiatives. This paper concludes with best practices and future directions for research in automating model retraining.

Downloads

Download data is not yet available.

References

Gayam, Swaroop Reddy. "Deep Learning for Autonomous Driving: Techniques for Object Detection, Path Planning, and Safety Assurance in Self-Driving Cars." Journal of AI in Healthcare and Medicine 2.1 (2022): 170-200.

Thota, Shashi, et al. "MLOps: Streamlining Machine Learning Model Deployment in Production." African Journal of Artificial Intelligence and Sustainable Development 2.2 (2022): 186-206.

Nimmagadda, Venkata Siva Prakash. "Artificial Intelligence for Real-Time Logistics and Transportation Optimization in Retail Supply Chains: Techniques, Models, and Applications." Journal of Machine Learning for Healthcare Decision Support 1.1 (2021): 88-126.

Putha, Sudharshan. "AI-Driven Predictive Analytics for Supply Chain Optimization in the Automotive Industry." Journal of Science & Technology 3.1 (2022): 39-80.

Sahu, Mohit Kumar. "Advanced AI Techniques for Optimizing Inventory Management and Demand Forecasting in Retail Supply Chains." Journal of Bioinformatics and Artificial Intelligence 1.1 (2021): 190-224.

Kasaraneni, Bhavani Prasad. "AI-Driven Solutions for Enhancing Customer Engagement in Auto Insurance: Techniques, Models, and Best Practices." Journal of Bioinformatics and Artificial Intelligence 1.1 (2021): 344-376.

Kondapaka, Krishna Kanth. "AI-Driven Inventory Optimization in Retail Supply Chains: Advanced Models, Techniques, and Real-World Applications." Journal of Bioinformatics and Artificial Intelligence 1.1 (2021): 377-409.

Kasaraneni, Ramana Kumar. "AI-Enhanced Supply Chain Collaboration Platforms for Retail: Improving Coordination and Reducing Costs." Journal of Bioinformatics and Artificial Intelligence 1.1 (2021): 410-450.

Pattyam, Sandeep Pushyamitra. "Artificial Intelligence for Healthcare Diagnostics: Techniques for Disease Prediction, Personalized Treatment, and Patient Monitoring." Journal of Bioinformatics and Artificial Intelligence 1.1 (2021): 309-343.

Kuna, Siva Sarana. "Utilizing Machine Learning for Dynamic Pricing Models in Insurance." Journal of Machine Learning in Pharmaceutical Research 4.1 (2024): 186-232.

Sengottaiyan, Krishnamoorthy, and Manojdeep Singh Jasrotia. "SLP (Systematic Layout Planning) for Enhanced Plant Layout Efficiency." International Journal of Science and Research (IJSR) 13.6 (2024): 820-827.

Venkata, Ashok Kumar Pamidi, et al. "Implementing Privacy-Preserving Blockchain Transactions using Zero-Knowledge Proofs." Blockchain Technology and Distributed Systems 3.1 (2023): 21-42.

Reddy, Amit Kumar, et al. "DevSecOps: Integrating Security into the DevOps Pipeline for Cloud-Native Applications." Journal of Artificial Intelligence Research and Applications 1.2 (2021): 89-114.

M. Abadi et al., "TensorFlow: A system for large-scale machine learning," in Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), 2016, pp. 265-283.

Y. Zhang and Q. Yang, "A survey on multi-task learning," IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 12, pp. 5586-5609, Dec. 2022.

Y. Wang, Q. Chen, and W. Zhu, "Zero-shot learning: A comprehensive review," IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 7, pp. 2172-2188, Jul. 2019.

Downloads

Published

09-10-2024

How to Cite

[1]
Emily Johnson, “Automating Model Retraining in DevOps Pipelines with MLOps: Addressing Model Drift and Data Evolution”, Distrib Learn Broad Appl Sci Res, vol. 10, pp. 317–323, Oct. 2024, Accessed: Nov. 07, 2024. [Online]. Available: https://dlabi.org/index.php/journal/article/view/157

Similar Articles

41-50 of 112

You may also start an advanced similarity search for this article.