MLOps Coffee Sessions #1: Serving Models with Kubeflow
MLOps.community
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Deploying and Customizing Machine Learning Models on Kubeflow
The chapter explores the challenges of deploying and customizing machine learning models, focusing on serving models on Kubeflow. It delves into the flexibility of using Kubeflow to serve various model formats like TensorFlow, PyTorch, and custom models, discussing deployment methods using Python SDK, Selden, and Kubernetes YAML files, as well as the concept of K-native serving for deploying serverless containers.
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