[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124057-en":3,"doc-seo-124057-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124057,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",6,"Technology","AI-driven devops - Leveraging Machine Learning for Automated Software Deployment and Maintenance","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.","OPEN ACCESS  \nEngineering Science & Technology Journal P-ISSN: 2708-8944, E-ISSN: 2708-8952  \nVolume 4, Issue 6, P.No. 728-740, December 2023 DOI: 10.51594/estj.v4i6 .1552  \nFair 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)  \nAI-driven devops: Leveraging machine learning for automated software deployment and maintenance  \nOyekunle Claudius Oyeniran 1, Adebunmi Okechukwu Adewusi2, Adams Gbolahan Adeleke3, Lucy Anthony Akwawa4, & Chidimma FranciscaAzubuko5  \n1Independent Researcher, North Dakota, USA  \n2Independent Researcher, Ohio, USA  \n3Leenit, UK  \n4Information Systems-Business Analytics Eastern Michigan University Ypsilanti, Michigan,  \nUSA  \n5Independent Researcher, Lagos, Nigeria  \n*Corresponding Author: Oyekunle Claudius Oyeniran  \nCorresponding Author Email: [claudiusoyekunle@gmail.com](claudiusoyekunle@gmail.com)  \nArticle Received: 25-09-23 Accepted: 05-11-23 Published: 30-12-23  \nLicensing Details: Author retains the right of this article. The article is distributed under the terms of  \nthe Creative Commons Attribution-NonCommercial 4.0 License  \n([http://www.creativecommons.org/licences/by-nc/4.0/](http://www.creativecommons.org/licences/by-nc/4.0/)) which permits non-commercial use,  \nreproduction and distribution of the work without further permission provided the original work is attributed as specified on the Journal open access page.  \nABSTRACT  \nThe 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  \ntechnologies 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.  \nKeywords: AI-driven DevOps, Machine Learning, Automated Software Deployment, Continuous Integration, Continuous Delivery, Predictive Analytics.  \nINTRODUCTION  \nDevOps, 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","cbCaijE6VMAz3aUs","https://ap.wps.com/l/cbCaijE6VMAz3aUs","pdf",587286,1,13,"English","en",105,"# Introduction\n## Core DevOps concepts and CI/CD\n## Limitations driving AI/ML adoption\n# AI-driven DevOps value and use cases\n## Predictive analytics and anomaly detection\n## Automated remediation and self-healing\n# Tools, technologies, and challenges\n## ML frameworks and observability platforms\n## Data quality, integration complexity, and ethics\n# Future direction: toward autonomous operations","[{\"question\":\"What does DevOps emphasize in the software development lifecycle?\",\"answer\":\"DevOps focuses on collaboration between developers and IT operations to remove silos and enable a more seamless, efficient, and rapid delivery process.\"},{\"question\":\"How do CI and CD contribute to modern DevOps practices?\",\"answer\":\"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.\"},{\"question\":\"How does AI-driven DevOps improve deployment and maintenance compared with traditional CI/CD?\",\"answer\":\"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.\"}]","AI-driven devops - 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