Automating Infrastructure Management for MLOps in DevOps Environments: A Cloud-Native Approach
Keywords:
MLOps, DevOpsAbstract
The increasing complexity of machine learning operations (MLOps) in production environments necessitates the automation of infrastructure management to enhance efficiency, scalability, and reliability. This paper explores the role of cloud-native technologies in automating infrastructure management within DevOps frameworks, focusing on how these technologies streamline resource allocation, scaling, and monitoring of machine learning models. By integrating containerization, orchestration, and serverless computing, organizations can achieve a seamless and responsive infrastructure that adapts to the dynamic demands of machine learning workloads. The discussion includes a review of best practices for implementing cloud-native solutions, challenges faced in automation, and strategies for overcoming these obstacles. Ultimately, the paper emphasizes the transformative potential of automating infrastructure management for MLOps in modern DevOps environments.
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