# Automating the Training and Deployment of Models in MLOps by Integrating Systems with Machine Learning

arXiv: https://arxiv.org/abs/2405.09819 · HTML: https://mlsanity.com/paper/2405.09819/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 17 references that are in the map.
- First posted on arXiv 16 May 2024.

## Builds on (most cited first, 4 of 4)

- [Machine Learning Operations (MLOps): Overview, Definition, and Architecture](https://mlsanity.com/paper/2205.02302/) (2022)
- [Progress in artificial intelligence applications based on the combination of self-driven sensors and deep learning](https://mlsanity.com/paper/2402.09442/) (2024)
- [The Essential Deployment Metamodel: A Systematic Review of Deployment Automation Technologies](https://mlsanity.com/paper/1905.07314/) (2019)
- [Characterising resource management performance in Kubernetes](https://mlsanity.com/paper/2401.17125/) (2024)

## Built on by (most cited first, 1 of 1)

- [What Artificial Intelligence can do for High-Performance Computing systems?](https://mlsanity.com/paper/2602.00014/) (2026)

## Abstract (arXiv)

This article introduces the importance of machine learning in real-world applications and explores the rise of MLOps (Machine Learning Operations) and its importance for solving challenges such as model deployment and performance monitoring. By reviewing the evolution of MLOps and its relationship to traditional software development methods, the paper proposes ways to integrate the system into machine learning to solve the problems faced by existing MLOps and improve productivity. This paper focuses on the importance of automated model training, and the method to ensure the transparency and repeatability of the training process through version control system. In addition, the challenges of integrating machine learning components into traditional CI/CD pipelines are discussed, and solutions such as versioning environments and containerization are proposed. Finally, the paper emphasizes the importance of continuous monitoring and feedback loops after model deployment to maintain model performance and reliability. Using case studies and best practices from Netflix, the article presents key strategies and lessons learned for successful implementation of MLOps practices, providing valuable references for other organizations to build and optimize their own MLOps practices.