AI-driven devops - Leveraging Machine Learning for Automated Software Deployment and Maintenance

Aug 4, 2026

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The integration of artificial intelligence and machine learning into DevOps practices is reshaping how software is deployed and maintained. Traditional CI/CD approaches can face scalability limits, error-prone operations, and persistent reliance on human oversight. AI-driven DevOps adds intelligent automation through predictive analytics, anomaly detection, and self-healing infrastructure to predict deployment outcomes, surface issues in real time, and automatically remediate problems. The paper reviews DevOps limitations, key AI/ML use cases, enabling tools and technologies, major challenges, and a trajectory toward more autonomous deployment and maintenance ecosystems.

LanguageEnglish

CategoryTechnology

FormatPDF

Pages13

Reading Time33 min

AccessFree to download

Introduction

Core DevOps concepts and CI/CD

Limitations driving AI/ML adoption

AI-driven DevOps value and use cases

Predictive analytics and anomaly detection

Automated remediation and self-healing

Tools, technologies, and challenges

ML frameworks and observability platforms

Data quality, integration complexity, and ethics

Future direction: toward autonomous operations

OPEN ACCESS Engineering Science & Technology Journal P-ISSN: 2708-8944, E-ISSN: 2708-8952 Volume 4, Issue 6, P.No. 728-740, December 2023 DOI: 10.51594/estj.v4i6 .1552 Fair East Publishers [Journal Homepage: ](Journal Homepage: www.fepbl.com/index.php/estj)[www.fepbl.com/index.php/estj](Journal Homepage: www.fepbl.com/index.php/estj) AI-driven devops: Leveraging machine learning for automated software deployment and maintenance Oyekunle Claudius Oyeniran 1, Adebunmi Okechukwu Adewusi2, Adams Gbolahan Adeleke3, Lucy Anthony Akwawa4, & Chidimma FranciscaAzubuko5 1Independent Researcher, North Dakota, USA 2Independent Researcher, Ohio, USA 3Leenit, UK 4Information Systems-Business Analytics Eastern Michigan University Ypsilanti, Michigan, USA 5Independent Researcher, Lagos, Nigeria *Corresponding Author: Oyekunle Claudius Oyeniran Corresponding Author Email: [claudiusoyekunle@gmail.com](claudiusoyekunle@gmail.com) Article Received: 25-09-23 Accepted: 05-11-23 Published: 30-12-23 Licensing Details: Author retains the right of this article. The article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License ([http://www.creativecommons.org/licences/by-nc/4.0/](http://www.creativecommons.org/licences/by-nc/4.0/)) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the Journal open access page. ABSTRACT The integration of artificial intelligence (AI) and machine learning (ML) into DevOps practices is revolutionizing software deployment and maintenance, paving the way for more efficient, reliable, and scalable systems. Traditional DevOps, characterized by continuous integration and continuous delivery (CI/CD), often struggles with scalability, error-prone processes, and the need for constant human oversight. AI-driven DevOps introduces intelligent automation, enabling predictive analytics, anomaly detection, and self-healing infrastructure. By leveraging AI/ML, organizations can predict deployment outcomes, identify potential issues in real time, and automatically rectify them, reducing downtime and enhancing overall system performance. This paper explores the current state of DevOps, highlighting its limitations and the transformative potential of AI/ML integration. We discuss key AI/ML use cases in DevOps, such as automated code quality analysis, predictive analytics for deployment, and self-healing systems. Additionally, we examine the tools and technologies that facilitate AI-driven DevOps, including ML frameworks like TensorFlow and observability platforms like Datadog. Despite its potential, AI-driven DevOps faces challenges, including data quality, integration complexity, and ethical considerations. The paper also looks into the future of AI in DevOps, envisioning a fully autonomous deployment and maintenance ecosystem. By addressing current challenges and embracing AI/ML technologies, organizations can significantly improve their DevOps processes, leading to faster, more reliable software delivery. Keywords: AI-driven DevOps, Machine Learning, Automated Software Deployment, Continuous Integration, Continuous Delivery, Predictive Analytics. INTRODUCTION DevOps, a portmanteau of "development" and "operations," represents a cultural and operational shift in the software development lifecycle. It emphasizes collaboration, communication, and integration between software developers (Dev) and IT operations (Ops) teams (Tatineni & Chinamanagonda, 2021) . The core idea behind DevOps is to break down the traditional silos between these two critical functions, enabling a more seamless, efficient, and rapid software delivery process. DevOps practices revolve around several key principles aimed at streamlining the development, deployment, and maintenance of software applications. CI/CD is the backbone of modern DevOps practices (Tatineni & Katari, 20

FAQs

What does DevOps emphasize in the software development lifecycle?

DevOps focuses on collaboration between developers and IT operations to remove silos and enable a more seamless, efficient, and rapid delivery process.

How do CI and CD contribute to modern DevOps practices?

Continuous Integration automates frequent code integration with builds and tests, while Continuous Delivery automates the release process by deploying to staging and sometimes production after tests pass.

How does AI-driven DevOps improve deployment and maintenance compared with traditional CI/CD?

AI-driven DevOps uses predictive analytics, anomaly detection, and self-healing infrastructure to predict deployment outcomes, identify issues in real time, and automatically rectify problems to reduce downtime and improve system performance.

Aug 4, 2026

Upload by Ezra