AI-Augmented CI/CD Pipeline Optimization for Scalable Cloud-Native Deployment
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V5I4P117Keywords:
AI In CI/CD, Cloud-Native Deployments, Devops Optimization, Continuous Integration (CI), Continuous Deployment (CD)Abstract
The paper argues about the AI-based optimization of the CI/CD pipelines to enhance the scalability and efficiency of the deployments in the cloud environment, whether it is non-native or not. CI/CD pipelines have also become essential in the automation of the integration, testing, and deployment processes, due to the high observed rate of software development. Nevertheless, due to the increasing complexity of cloud-native environments, the process of scaling CI/CD pipelines has become a significant issue. The paper discusses how AI technologies in the form of machine learning can be used to automate decision-making processes, reduce the number of errors, and enhance the efficient allocation of resources in these pipelines. Among the key findings of the deployment, there has been a significant improvement in the speed of deployment, and deployment times have been reduced by up to 30%; error rates have also been reduced by 35%. Also, AI has reduced the utilization of cloud resources by 15% due to its ability to minimize the usage of resources. The industries that need high scalability, like e-commerce, healthcare, and fintech, demand high-performance and reliable deployments, which are only possible with these advancements. The article demonstrates that AI can be helpful in CI/CD pipelines due to its ability to improve the reliability of deployments, make the process of troubleshooting simple and fast, and use resource-efficient cloud-native applications. The results indicate that companies that embrace AI-driven CI/CD pipelines will be better positioned to contend in more intricate clouds, and that provides a concise direction of advancement in DevOps and AI coordination
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