AI-Powered Machine Learning and Cloud-Native DevOps for Scalable ERP Systems: A Performance Evaluation of Online Automated Applications
DOI:
https://doi.org/10.15662/IJARCST.2024.0704005Keywords:
AI-Powered DevOps, Machine Learning, Cloud-Native Architecture, Scalable ERP Systems, Online Automated Applications, Predictive Analytics, Performance Evaluation, Intelligent Automation, Continuous Integration and Deployment (CI/CD), Enterprise Software EngineeringAbstract
This study presents a comprehensive framework for enhancing the scalability and performance of Enterprise Resource Planning (ERP) systems through the integration of AI-powered machine learning and cloud-native DevOps methodologies. The proposed architecture leverages predictive analytics, automated deployment pipelines, and intelligent workload management to optimize the lifecycle of online automated applications. By embedding machine learning algorithms within DevOps workflows, the framework enables adaptive performance tuning, proactive fault detection, and dynamic resource allocation across multi-cloud environments. A performance evaluation demonstrates significant improvements in system responsiveness, throughput, and operational resilience when compared to traditional ERP deployments. The research underscores the potential of AI-driven DevOps ecosystems to revolutionize enterprise software engineering by promoting automation, scalability, and continuous performance optimization in real-world ERP applications.
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