Modernizing Enterprise Application Architecture using Machine Learning Intelligent APIs and Cloud Native Computing
DOI:
https://doi.org/10.15662/IJEETR.2024.0606031Keywords:
Artificial Intelligence, Cloud Computing, Microservices, Enterprise Data Processing, Machine Learning, Intelligent Decision-Making, Cloud-Native Architecture, Scalability, Event-Driven Architecture, Big Data, Distributed Systems, Data GovernanceAbstract
The rapid growth of enterprise data, coupled with increasing demands for real-time analytics and intelligent decision-making, has created a need for scalable and adaptive software architectures. AI-enabled cloud microservices provide an architectural approach that combines the elasticity of cloud computing, the modularity of microservices, and the analytical capabilities of artificial intelligence. This essay examines the architecture and research methodology for developing AI-enabled cloud microservices capable of supporting scalable enterprise data processing and intelligent decision-making. The proposed architectural perspective integrates containerized microservices, cloud-native infrastructure, event-driven communication, distributed data processing, machine learning services, application programming interfaces, and automated monitoring. Particular attention is given to scalability, interoperability, security, resilience, data governance, and model lifecycle management. The literature review identifies how cloud computing, microservice architecture, big-data platforms, and AI have evolved independently and increasingly converged into integrated enterprise platforms. The research methodology adopts a mixed-method architectural evaluation involving systematic literature analysis, reference architecture development, prototype implementation, and performance experimentation. Key evaluation criteria include throughput, latency, resource utilization, scalability, fault tolerance, prediction accuracy, and decision-making effectiveness. The study aims to establish practical architectural principles for enterprises seeking to transform heterogeneous data into timely and reliable intelligent insights while maintaining operational flexibility and governance
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