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

Aug 3, 2026

The paper examines why machine learning must be operationalized beyond experimentation, focusing on the transition of models from lab settings to production. It reviews the rise of MLOps and its connection to traditional software practices, then proposes system integration approaches to improve productivity and address gaps in deployment and performance monitoring. Emphasis is placed on automated training, transparency and repeatability via version control, and integration challenges with CI/CD pipelines, including environment versioning and containerization. It further highlights continuous monitoring and feedback loops after deployment and provides lessons and strategies drawn from Netflix to support reliable, maintainable MLOps adoption.

LanguageEnglish

CategoryTechnology

FormatPDF

Pages11

Reading Time28 min

AccessFree to download

Introduction

Challenges moving models to production

MLOps definition and goals

Related Work

Development of MLOps

Evolution from DevOps to MLOps

Automating the Training and Deployment of Models in MLOps by Integrating Systems with Machine Learning Penghao Liang1*,Bo Song1,2 ,Xiaoan Zhan1,3 ,Zhou Chen2 ,Jiaqiang Yuan3 1* Information Systems,Northeastern University,San Jose, CA ,USA 1.2 Computer Science,Northeastern University, Boston, MA,USA 1.3 Electrical Engineering, New York University, NY, USA 2 Software Engineering,Zheiiang University,Hangzhou.China 3 Information Studies, Trine University, Phoenix, AZ,USA *Corresponding author:Penghao Liang,-[mail:liang.p@northeastern.edu](mail:liang.p@northeastern.edu) Abstract. 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. Keywords: Machine learning; MLOps; Automated deployment; CI/CD pipeline; Supervisory control 1. Introduction Machine learning has revolutionized the way people use and interact with data, driving business efficiency, fundamentally changing the advertising landscape, and revolutionizing healthcare technology. Over the past decade, machine learning (ML) has become an essential part of countless applications and services in a variety of fields. Thanks to the rapid development of machine learning, there have been profound changes in many fields, from health care to autonomous driving. However, the increasing importance of machine learning in practical applications also brings new challenges and problems, especially when it comes to moving models from a laboratory environment to a production environment. Traditional software development and operations methods often fail to meet the specific needs of machine learning models in production, resulting in challenges such as the complexity of model deployment, difficulties in performance monitoring, and the absence of continuous integration and continuous deployment [1](CI/CD) processes. To address these issues, attention is being paid to an emerging field called Machine Learning System Operations (MLOps) . MLOps is a relatively new term that has gradually gained traction over the past few years. It closely links computer systems and machine learning and considers new challenges in machine learning from the perspective of traditional systems research. [2]MLOps is not just a tool or process, it is a philosophy and methodology that aims to achieve continuous delivery and reliable operation of machine learning models. Against this background, this article will explore ways to automate model training and deployment by integrating systems with machine learning. First, we will review the challenges and problems in existing MLOps, and then lead to the topic of this article, which is how the integration of systems with machine learning can solve these challenges and improve productivity. 2. Related Work 2.1. Review on the developm

FAQs

What problems does the paper highlight when deploying machine learning models to production?

It notes that traditional software operations often fail for ML needs, leading to deployment complexity, limited performance monitoring, and missing continuous integration/continuous deployment processes.

How does the paper propose improving automated model training and reproducibility?

It emphasizes automation for model training and ensuring transparency and repeatability through version control of the training process.

What solutions are discussed for integrating machine learning into CI/CD pipelines?

The paper discusses versioning environments and using containerization to bridge ML components with conventional CI/CD workflows.

Aug 3, 2026

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