Aug 2026· The American Journal of Engineering and Technology· Vol 08, pp. 31-42· 0 citations
TL;DR
This study aims to create an AI/ML based DevOps automation framework for deployment risk prediction, deployment optimization, rollback management, and continuous monitoring in a single CI/CD workflow.
Abstract
The integration of Artificial Intelligence (AI) and Machine Learning (ML) is transforming DevOps, enabling it to become a predictive, intelligent, and adaptive process across the software delivery lifecycle. This study aims to create an AI/ML based DevOps automation framework for deployment risk prediction, deployment optimization, rollback management, and continuous monitoring in a single CI/CD workflow. The framework’s data components are the build logs, deployment history, infrastructure monitoring, configuration repositories, incident records, and user feedback, which are then combined with DevOps data into a machine learning pipeline. The data is then preprocessed, combined, and processed by feature engineering to be split into training, validation, and testing sets. A Random Forest model is employed for deployment risk prediction, while an AI-driven decision engine uses predicted risk and operational conditions to support deployment optimization, resource allocation, automated rollback, and continuous monitoring. Experimental evaluation demonstrates a 50% reduction in average build time, a 60% reduction in deployment failure rate, a 50% reduction in rollback frequency, a 34% improvement in deployment risk prediction accuracy, and a 68% reduction in false positive rate. The findings demonstrate improved software delivery efficiency, reliability, operational stability, and decision-making.
This paper explores the integration of artificial intelligence techniques into software engineering practices to optimize distributed systems for scalable machine learning (ML) workflows. As ML models grow in complexity and data volume, traditional system design approaches struggle to meet the demands of performance, scalability, and resource efficiency. We propose an AI-driven framework that leverages predictive analytics, automated resource management, and intelligent scheduling to enhance distributed computing environments. The study examines key challenges in distributed ML systems, including data partitioning, workload balancing, fault tolerance, and latency optimization. Through a combination of simulation and real-world case studies, we demonstrate how AI-based optimization strategies improve system throughput, reduce training time, and enhance resource utilization. The results highlight the potential of combining software engineering principles with AI-driven decision-making to build resilient and efficient ML infrastructures. This work contributes a structured approach for designing next-generation distributed systems capable of supporting large-scale machine learning applications.
Yuki Tanaka· International Journal of Art...· 0 citations
The findings advocate for the integration of AI-powered pipelines within ERP systems as a transformative approach to enable scalable, intelligent, and high-fidelity data processing, essential for next- generation enterprise software resilience and performance.
Yuvaraj Kavala· International Journal of Com...· 0 citations
The rapid evolution of enterprise architecture necessitates innovative approaches to manage the increasing complexities of digital ecosystems. This paper explores the transformative potential of Artificial Intelligence (AI)-driven cloud solutions in modernizing enterprise architecture, with a focus on integrating DevOps and DataOps methodologies to achieve scalability. AI-powered tools and frameworks in cloud computing offer unparalleled scalability, operational efficiency, and real-time adaptability, enabling enterprises to remain competitive in a data-driven economy. By combining DevOps' focus on streamlining software development and operations with DataOps' emphasis on agile and automated data pipeline management, organizations can optimize workflow automation, accelerate deployment cycles, and enhance decision-making processes. AI further augments this synergy by facilitating predictive analytics, anomaly detection, and intelligent resource allocation, which are critical for achieving scalability and reliability in dynamic business environments. Case studies highlight the successful application of these technologies across various industries, showcasing measurable improvements in performance and cost efficiency. The paper also addresses challenges in adopting AI-driven cloud solutions, including data privacy, compliance, and skill gaps, offering actionable recommendations for mitigating these obstacles. Emphasis is placed on the need for collaborative strategies between IT and business teams to maximize the potential of integrated DevOps and DataOps frameworks.
Fatou Diop· International Journal of Art...· 0 citations
The analysis indicates that combining predictive defect-risk scores with automated test selection can potentially reduce redundant testing, concentrate computational resources on high-risk software components, and improve feedback speed, but model reliability depends on historical defect data, feature quality, distributional stability, explainability, and integration with existing development pipelines.
Haruto Tanaka, Yuki Nakamura· Frontiers in Emerging Multid...· 0 citations
The foundations of DevOps enable rapid software development and deployment by a variety of organizations. Continuous integration, continuous delivery, infrastructure as code, and configuration as code are now mature concepts. However, DevOps techniques remain largely unexplored in the context of cloud-native financial automation. By investigating financial automation using these techniques, a framework for integrating financial automation throughout the BANK Financial Evolution through Reinvention program, especially into credit risk-related workstreams, is derived. To support such integration, continuous delivery techniques for regulated environments, leveraging the cloud-native architecture of the Program’s ecosystem, are presented. Cloud-native architectural patterns related to supporting automated financial processes are also proposed, making use of the concepts of microservices, service meshes, serverless computing, and data mesh. Furthermore, Agentic Artificial Intelligence (Agentic AI) concepts are applied to financial automation, especially in relation to agentic roles and the levels of autonomy of Agentic AI. Finally, an exploration of generative intelligence’s role in supporting and facilitating the work of human agents who undertake financial automation is undertaken by considering the automation of a variety of workflows including report generation, the creation and maintenance of compliance documentation, and providing audit trails to help support compliance and validation activities.
Emily A. Carter· International Journal of Mod...· 0 citations
This review examines recent progress in large AI models for intelligent manufacturing, covering model architectures, adaptation strategies, system integration, and applications across product development, production processes, equipment maintenance, and manufacturing services.
Baotong Chen, Lu Dai, Chuangjian Wang et al.· IEEE Access· 0 citations
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