Scalable Deployment of Machine Learning Models on Kubernetes Clusters: A DevOps Perspective
DOI:
https://doi.org/10.15662/IJRAI.2024.0706003Keywords:
Kubernetes, machine learning, DevOps, scalable deployment, CI/CD, containerization, automation, model servingAbstract
The growing adoption of machine learning (ML) in enterprise and telecom environments demands scalable, reliable, and automated deployment strategies. Kubernetes has emerged as a de facto standard for orchestrating containerized applications, offering elasticity, self-healing, and workload portability. From a DevOps perspective, integrating ML model deployment within Kubernetes clusters requires a seamless workflow that incorporates CI/CD practices, monitoring, and infrastructure automation. This paper explores scalable deployment strategies for ML models on Kubernetes, emphasizing containerization, pipeline automation, and resource optimization. The proposed DevOps-driven framework leverages tools such as Kubeflow, KServe, and Helm to streamline model serving, ensure reproducibility, and support dynamic scaling under variable workloads. Experimental analysis demonstrates reductions in deployment time, improved resource utilization, and enhanced system reliability. The findings establish a roadmap for operationalizing ML at scale, enabling organizations to achieve faster innovation cycles and resilient AI-driven services.
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