MLOps Coffee Sessions #1: Serving Models with Kubeflow
MLOps.community
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Optimizing Model Serving with Kubeflow
This chapter delves into the practical applications of Kubeflow serving, particularly in real-time prediction scenarios using examples like Netflix. It emphasizes the benefits of serverless aspects for cost-effectiveness and scalability, while also discussing challenges and solutions related to setting up infrastructure and servicing machine learning models efficiently. The importance of understanding Kubernetes, managing pods, creating Docker files, and troubleshooting issues effectively is highlighted throughout the conversation.
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