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American Journal of Applied Science and Technology

Peer Reviewed | Open Access | E-ISSN: 2771-2745
Published Article

Machine Learning-Driven DevOps: A Unified Framework for Autonomous Software Operations

Machine Learning-Driven DevOps: A Unified Framework for Autonomous Software Operations

Authors

  • Frederick J. Stonebridge
    Faculty of Engineering, University of Oslo, Norway

Keywords:

AI driven DevOps AIOps intelligent automation machine learning operations

Abstract

The accelerating complexity of modern software systems, driven by cloud native architectures, microservices, continuous integration and continuous deployment pipelines, and data intensive artificial intelligence workloads, has created a structural transformation in how software is designed, delivered, and governed. DevOps emerged as a response to this complexity by integrating development and operations into a unified lifecycle, yet traditional DevOps practices increasingly struggle to manage the scale, velocity, and uncertainty inherent in contemporary digital infrastructures. Artificial intelligence, particularly in the form of machine learning driven automation, has consequently become a central force in the evolution of DevOps into what is now widely referred to as AIOps and intelligent DevOps. This article develops a comprehensive, publication ready analysis of how AI driven automation reshapes software engineering, operations, governance, and organizational value creation, synthesizing insights from software engineering research, machine learning systems theory, enterprise architecture, and economic studies of AI adoption. Grounded in the conceptual foundations articulated by Varanasi (2025) regarding AI driven DevOps pipelines, this study integrates broader literature on data preparation, technical debt, neural architecture search, predictive maintenance, bias mitigation, and enterprise automation to construct a unified theoretical framework for intelligent DevOps ecosystems.

 

Ultimately, this article concludes that AI driven DevOps is not simply an incremental improvement of existing practices but a foundational reconfiguration of software engineering as a discipline. By embedding learning systems into every layer of the software lifecycle, organizations move toward continuously adaptive digital infrastructures that are capable of anticipating failures, optimizing performance, and aligning technological operations with business value in real time, as articulated by Falcioni (2024) and OBrien et al. (2018). This transformation, however, requires rigorous governance, high quality data pipelines, and a rethinking of professional roles in software engineering to ensure that algorithmic intelligence remains aligned with human values and organizational objectives.

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