Masters Degree in Computer Science.
Research Article
World Journal of Advanced Research and Reviews, 2023, 19(01), 1593-1606
Received on 12 January 2023; revised on 22 July 2023; accepted on 25 July 2023
The application of AI into DevOps process brought about a significant change to how software development, deployment and operation is dealt with. Robotic process automation, machine learning, systems intelligence, and analytical tools are essential in increasing productivity, while decreasing vulnerability and failure rate at every level of DevOps. This paper focuses on the role of AI in DevOps today, the enhancement of automation and predictive performance, system reliability to meeting challenges, limitations and risks involved. AI factors in DevOps are multipurpose and involves using machine learning algorithms, predictive analyzes, and real-time monitoring systems. The use of predictive analytics helps AI to assist the DevOps teams in the identification of the potential failure rates of different systems through machine learning time-series and neural analysis along with regression modeling. These models improve the decision-making process since they promote intervention before problems affect the system’s operations. Third, AI-based systems in the incident response process enable the reduction of business unproductiveness since system issues can be corrected through self-healing design and root cause analysis of the incident. This helps to decrease mean time to repair (MTTR) since problems are identified and solved without delay, which improves total systematic availability. With the help of AI the levels of redundancy, fault tolerance and ability to automatically recover are embedded into the system architecture hence enhancing system resilience. AI re Christens these historic themes of resilience by identifying future failures and taking protective measures before that happens. Chaos Monkey of Netflix and Borg of Google are some good examples in which AI makes better enhancements to fault tolerant mechanisms, allocation of dynamic resources and failure handling at runtime environments. From these case studies, one can see that AI is being used to build up system resilience, thus maintaining the constant supply and avoiding failure. But all is not lost, integration between AI and DevOps practices has its own set of concerns. Cognitive factors; reasons including tool interoperability issues, ethical issues such as bias in AI based decision-making systems, and organizational inertia due to skill deficits are deemed major challenges. Security risks especially those that may arise due to incorporation of AI tools which are likely to have security risks, should therefore be dealt proactively to prevent compromise of the whole system. This paper also identifies trends on how the future of DevOps will be influenced by AI while recommending that more empirical studies should be conducted to fill the various gaps that have been noted, address issues of tool interoperability and ensure AI offers a secure and scalable way of developing DevOps. Based on an analysis of AI in the context of DevOps, this paper provides an overview of the best practices and directions for AI implementation in IT operations while indicating the existing research limitations with AI used in IT operations.
Artificial Intelligence (AI); DevOps; Predictive Analytics; Automation; Machine Learning; Incident Response; System Resilience
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Osinaka Chukwu Desmond. Transforming DevOps with artificial intelligence: A deep dive into intelligent automation, predictive analytics, and resilient system design. World Journal of Advanced Research and Reviews, 2023, 19(1), 1593-1606. Article DOI: https://doi.org/10.30574/wjarr.2023.19.1.0113