Secure and Scalable Artificial Intelligence Platforms UsingMicroservices and Container Orchestration
Keywords:
Artificial Intelligence, Microservices, Container Orchestration, Kubernetes, Scalable AI, Cloud-native ArchitectureSynopsis
Purpose: This study investigates the development of secure, scalable artificial intelligence (AI) platforms using microservices architecture and container orchestration technologies.
Design/methodology/approach: Through a synthesis of prior research and architecture design patterns, this paper explores how container orchestration (e.g., Kubernetes) and microservices facilitate the flexible deployment of AI models in dynamic environments.
Findings: The integration of AI with microservices and orchestration platforms increases scalability, modularity, and resilience while enabling better resource utilization and streamlined updates.
Practical implications: These insights can be applied to designing robust, cloud-native AI platforms for industries requiring real-time learning, scalable deployment, and secure data handling.
Originality/value: This work offers a consolidated view of pre-2020 literature on microservices and orchestration in AI contexts, highlighting architectural best practices and gaps for future research.
References
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