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Evolution Of Intelligent Devops Systems: Analytical Approaches To AI-Driven Automation And Cognitive IT Operations At Scale
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AIOps
Intelligent
DevOps
Cognitive IT Operations
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Abstract
The evolution of DevOps practices has transitioned from reactive operational models to increasingly autonomous, AI-driven, and cognitively enhanced systems capable of managing large-scale distributed infrastructures. This paper examines the analytical progression of intelligent DevOps systems, focusing on the integration of machine learning, predictive analytics, and automation frameworks in modern IT operations. The study synthesizes existing research on AIOps, log analytics, anomaly detection, and incident management to establish a conceptual understanding of how cognitive capabilities are reshaping DevOps ecosystems. Core attention is given to the role of predictive operational intelligence in reducing downtime, optimizing system performance, and enabling proactive decision-making in cloud-scale environments. The paper further explores governance challenges, data dependency issues, and automation maturity levels influencing system reliability. Through comparative analysis of prior studies, the research identifies key gaps in scalability, cross-system generalization, and explainability of AI-driven operational decisions. Findings suggest that intelligent DevOps systems significantly enhance operational resilience but require robust governance frameworks and standardized data pipelines to ensure reliability at scale. The study contributes to the theoretical and practical understanding of AI-enabled DevOps transformation and outlines future directions for building self-adaptive cognitive IT operations frameworks.
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