Deploying a machine learning application on a modern distributed system puts the spotlight on reliability, performance, security, and other operational concerns. In this in-depth guide, Yuan Tang, project lead of Argo and Kube-flow, shares patterns, examples, and hard-won insights on taking an ML model from a single device to a distributed cluster. Distributed machine learning patterns provides dozens of techniques for designing and deploying distributed machine learning systems. In it, you'll learn patterns for distributed ...
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Deploying a machine learning application on a modern distributed system puts the spotlight on reliability, performance, security, and other operational concerns. In this in-depth guide, Yuan Tang, project lead of Argo and Kube-flow, shares patterns, examples, and hard-won insights on taking an ML model from a single device to a distributed cluster. Distributed machine learning patterns provides dozens of techniques for designing and deploying distributed machine learning systems. In it, you'll learn patterns for distributed model training, managing unexpected failures, and dynamic model serving. You'll appreciate the practical examples that accompany each pattern along with a full-scale project that implements distributed model training and inference with autoscaling on Kubernetes.
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