AI-Driven Predictive Deployment Pipeline for AEM as a Cloud Service
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V3I2P116Keywords:
AEM as a Cloud Service, CI/CD, Predictive Deployment, AI-powered DevOps, Adobe Experience Manager, Machine Learning for Deployment, Cloud Automation, Drift Detection, Risk Scoring, Release OptimizationAbstract
Adobe Experience Manager as a Cloud Service (AEMaaCS) has changed the way businesses build & deliver these digital experiences. However, its highly automated, cloud-native architecture makes CI/CD more difficult because it requires frequent code deployments, strict deployment schedules, continuous platform updates & complicated dependency behaviors that can make these deployment cycles unpredictable. Conventional pipelines usually only fix problems after they happen, which slows down release speed because of delays, rollbacks, and many other inefficiencies. This paper introduces an AI-driven predictive deployment pipeline designed to improve their intelligence, foresight, and adaptive decision-making in AEMaaCS release processes. Before the deployment is run, the pipeline uses machine-learning models to look at historical deployment logs, code modification metadata, AEM health indicators & environmental trends to figure out how likely it is that the build will succeed, the content will regress, the performance will drop, or the environment will become unstable. It works seamlessly with existing DevOps workflows, adding predictive scoring, automatic risk classification, and dynamic deployment strategies like auto-pausing, optimizing rollout timings, or proposing code changes. The experimental setup tests the system with actual deployment datasets and simulated cloud service environments. It shows huge improvements in deployment reliability, fewer failed builds, faster recovery times. The results suggest that forecasting scoring can help teams stay clear of high-risk releases with a precision of up to 85% and decrease the number of pipeline failures by recognizing problems early on. The proposed approach improves AEMaaCS DevOps through shifting how they handle implementations from a reactive to a more proactive way of making decisions using the information they have. The contributions include the latest predictive architecture made for AEMaaCS, a whole approach for forecasting deployment behavior & an empirical study that shows how useful it is in actual life. This research aims to empower teams to accomplish more intelligent, secure, and efficient releases, hence enhancing time-to-market and improving the reliability of digital experience delivery inside modern cloud ecosystems.
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