AI-Enhanced DevOps for Intelligent Multi-Cloud Software Deployment and Maintenance: A Comprehensive Analysis
Keywords:
AI-Driven DevOps, Machine Learning, Software Deployment, Anomaly DetectionAbstract
The evolution of software engineering has increasingly embraced artificial intelligence (AI) as a transformative tool, particularly within the domain of DevOps. AI-driven DevOps practices promise automation, predictive maintenance, and enhanced operational efficiency, facilitating a paradigm shift from reactive to proactive software lifecycle management. This research explores the integration of machine learning (ML) techniques in modern DevOps frameworks, emphasizing deployment automation, anomaly detection, and continuous monitoring. By synthesizing findings from a broad spectrum of studies on multivariate time series anomaly detection, online learning systems, and cloud-based operations, this study situates AI as both a catalyst and a challenge within contemporary software engineering practices (Varanasi, 2025). A particular focus is given to real-time streaming data anomaly detection, causal inference models, and human-in-the-loop approaches to machine learning, highlighting their theoretical underpinnings and practical applications in DevOps contexts (Li et al., 2021; Su et al., 2019; Ahmad et al., 2017). Methodologically, the study employs a qualitative meta-analytical approach, synthesizing empirical findings, technical reports, and case studies to construct a coherent narrative of AI integration within DevOps pipelines. Results reveal the dual benefits of intelligent automation: enhanced deployment reliability and reduction in operational costs. However, challenges persist in scalability, interpretability, and integration with multi-cloud environments (Goswami et al., 2021; Pum, 2024). The discussion critically evaluates the interplay between AI-driven decision-making and human oversight, emphasizing the necessity of explainable AI models to ensure accountability in automated DevOps practices. Concluding, the research provides strategic recommendations for practitioners seeking to implement AI-augmented DevOps, outlining a roadmap for future research that addresses current limitations while capitalizing on AI’s predictive and adaptive potential. This study contributes to the literature by offering a detailed, theoretically grounded examination of AI-driven DevOps in multi-cloud software ecosystems, reinforcing the imperative for ongoing interdisciplinary research.
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