The article investigates the efficiency of DevOps processes by integrating artificial intelligence and machine learning technologies. Traditional DevOps approaches face limited monitoring, the complexity of managing CI/CD processes, a significant amount of logs and incidents, and inefficient scaling of resources in cloud environments. Attention is focused on implementing intelligent methods for analyzing logs, predicting failures, automating software deployment, and optimising load balancing. Particular attention is paid to deep learning, natural language processing, and time series forecasting algorithms to improve anomaly detection and provide more accurate load forecasting. Mechanisms for optimising CI/CD processes using reinforcement learning, which allows for automatically adapting pipeline settings to changing operating conditions, are also discussed. Using clustering and pattern recognition methods in the code allows the detection of potential errors even before the testing stage, significantly reducing the risk of failure when deploying new versions. The introduction of intelligent monitoring systems allows for the prompt detection of deviations from normal operation, which facilitates proactive problem solving and minimises human intervention at critical moments. The study also considers prospects for further development, including developing integrated platforms that combine different AI models for monitoring, resource management, and deployment automation. In addition, the article analyses the application of generative AI models for coding automation, including the creation of optimized test scenarios and automatic correction of errors in the code. Particular attention is paid to the possibility of creating autonomous DevOps agents that can independently manage the software life cycle, analyse the effectiveness of deployments, and make decisions on rolling back changes or further optimising resources.
Дуда et al. (Fri,) studied this question.