Intelligent Automation: Leveraging LLMs in DevOps Toolchains
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V5I4P109Keywords:
DevOps, LLMs, Intelligent Automation, CI/CD, Infrastructure as Code, AIOps, Software Engineering, MLOps, Pipeline Optimization, GitOpsAbstract
Core of this transformation, large language models (LLMs) enable intelligent automation to be a necessary facilitator of speed, quality, and creativity in the modern DevOps environment. In fields including code generation, testing, deployment, and incident response, these advanced artificial intelligence technologies are changing the tools applied by development and operations teams. By understanding natural language directions, creating scripts, extracting insights from logs, and suggesting security or performance improvements, Large Language Models (LLMs) enable easy integration with modern DevOps toolchains to automate knowledge-intensive processes. Driven by LLMs, intelligent automation not only reduces manual work but also boosts human capacities, therefore enabling teams to quickly make more educated decisions. From code completions and reviews to test cases or deployment scripts, LLMs enhance development; they increase CI/CD by helping with planning across the DevOps lifecycle; and they enable monitoring and troubleshooting by telemetry data analysis. LLMs propose architecture and interpret specifications. A well-known case study demonstrates how LLMs are included into a CI/CD pipeline of a mid-sized technology company, therefore reducing manual participation and considerably raising deployment frequency. This change shows how effectively including LLMs into present DevOps methods produces output and advances proactive problem-solving culture. This abstract investigates the human-centric, pragmatic impacts of LLM-driven automation, therefore improving DevOps to be not only more intelligent but also more efficient
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