SELF-LEARNING DEVOPS PIPELINES: USING REINFORCEMENT LEARNING TO DYNAMICALLY TUNED PIPELINES

Abstract

The article explores the possibilities of using reinforcement learning methods. Learning (RL) for creating self-optimizing DevOps pipelines. Such systems are capable of dynamically adapting pipeline parameters based on current workload, quality metrics, and business requirements. This paper examines the architectural aspects of integrating RL algorithms into the DevOps ecosystem, as well as practical scenarios aimed at optimizing the build, testing, and delivery times of software products. A comparative analysis of the advantages and limitations of the proposed approach versus traditional automation methods is provided.