The Algorithmic Transformation of Devops: Synthesizing Artificial Intelligence, Machine Learning, And Site Reliability Engineering for Autonomous Cloud-Native Systems
Keywords:
DevOps, Artificial Intelligence, Site Reliability Engineering, Automated Program RepairAbstract
The rapid maturation of DevOps as a foundational paradigm for modern software development has reached a critical inflection point characterized by the integration of artificial intelligence and machine learning. This research article provides an exhaustive analysis of the evolution of DevOps from manual, process-oriented workflows toward autonomous, AI-driven architectures. By synthesizing literature from DORA (DevOps Research and Assessment) reports, tertiary studies on DevOps adoption, and cutting-edge research in automated program repair and resource optimization, this study examines the technological, cultural, and organizational challenges inherent in this transition. We investigate the deployment of long short-term memory (LSTM) and seasonal autoregressive integrated moving average (SARIMA) models for predictive cluster resource management and explore the role of AI in streamlining site reliability engineering (SRE) through automated incident management. The research further evaluates success factors for DevOps adoption-utilizing complex fuzzy sets and multi-criteria decision-making frameworks-to prioritize organizational readiness. Our analysis reveals that while AI-driven DevOps promises substantial improvements in deployment frequency, lead time for changes, and mean time to recovery (MTTR), it also introduces significant complexities regarding model interpretability, technical debt in ML production readiness, and the necessity for robust MLOps practices. This article provides a comprehensive framework for navigating these complexities, offering a roadmap for organizations seeking to leverage AI for autonomous software deployment and maintenance.
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Copyright (c) 2025 Julian Hamilton
This work is licensed under a Creative Commons Attribution 4.0 International License.
Individual articles are published Open Access under the Creative Commons Licence: CC-BY 4.0.
