Optimizing CI/CD Workflows With Machine Learning: Predictive Resource Allocation For Enhanced Deployment Efficiency

Abstract

This work established that predictive resource allocation employing machine learning enables CI/CD integration for streamlining deployment. Dissecting history, mathematical models are created to predict what resources the organization will require in the future, thereby minimizing redeployment issues and time. The simulation reports show rich benefits concerning optimizing resource usage and productivity enhancement. Actual-time cases support the method's applicability, demonstrating decreased resource wastage and deployment time. The following graphical data representations elaborate on these enhancements: Issues like the Model's Accuracy and variability of the data set are explained, and possible solutions are proposed. The paper outlines how ML can be incorporated into the CI/CD pipeline. It presents findings that can help organizations enhance the deployment function and possibly enhance the dependability of the software delivery procedure.

Published Date: 10 July 2022