A Scalable SAP HANA–Driven Real-Time AI Cloud and ERP DevOps Framework for Machine Learning, DeepLearning, and Cybersecurity
DOI:
https://doi.org/10.15662/IJARCST.2023.0603006Keywords:
SAP HANA, AI cloud architecture, ERP integration, DevOps framework, Real-time analytics, Machine learning, Deep learning, Cybersecurity, Threat detection, Predictive analytics, Anomaly detection, Cloud-based ERP, Scalable architecture, , Automated deployment, Enterprise operationsAbstract
Enterprises increasingly rely on cloud-based ERP systems and AI technologies to handle large-scale operations, real-time analytics, and cybersecurity challenges. This paper proposes a scalable SAP HANA–driven real-time AI Cloud and ERP DevOps framework that integrates machine learning (ML) and deep learning (DL) models for enhanced operational efficiency and threat detection. The framework leverages SAP HANA’s in-memory computing capabilities to process high-volume transactional and operational data in real time. ML and DL algorithms are employed for predictive analytics, anomaly detection, and cybersecurity threat identification across ERP and cloud environments. The DevOps integration ensures continuous deployment, automated testing, and monitoring, embedding security practices throughout the software lifecycle. Scalable cloud architecture supports flexible resource allocation, high availability, and seamless ERP interoperability. Experimental evaluation demonstrates improved threat detection accuracy, faster response times, and optimized resource utilization, making the framework suitable for modern enterprises aiming to enhance operational resilience, security, and AI-driven automation.References
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