{"id":42074,"date":"2024-05-14T00:41:06","date_gmt":"2024-05-14T07:41:06","guid":{"rendered":"https:\/\/devblogs.microsoft.com\/premier-developer\/?p=42074"},"modified":"2024-05-10T06:54:19","modified_gmt":"2024-05-10T13:54:19","slug":"azure-machine-learning-service-for-kubernetes-architects-deploy-your-first-model-on-aks-with-az-cli-v2","status":"publish","type":"post","link":"https:\/\/devblogs.microsoft.com\/premier-developer\/azure-machine-learning-service-for-kubernetes-architects-deploy-your-first-model-on-aks-with-az-cli-v2\/","title":{"rendered":"Azure Machine Learning Service for Kubernetes Architects: Deploy Your First Model on AKS with AZ CLI v2"},"content":{"rendered":"

Joseph Masengesho<\/a> provides a step-by-step tutorial on how to deploy your first ML model on AKS.<\/p>\n

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In a\u00a0previous post<\/a>, I provided a lengthy write-up about my understanding of using Kubernetes as a compute target in Azure ML from a Kubernetes architect\u2019s perspective. In this post, I will offer a step-by-step tutorial that teaches you how to deploy your first ML model on AKS. As a disclaimer, I am not a data scientist; however, I work with customers who deploy ML workloads on Kubernetes.<\/p>\n

In this tutorial, we will deploy a trained regression model based on the\u00a0MNIST Dataset<\/a>, which consists of 60K handwritten digits for training and 10K for testing. The model was created using the\u00a0scikit-learn framework<\/a>. You can learn more about how to use it in Azure ML\u00a0here<\/a>. We will use\u00a0Azure CLI v2<\/a>\u00a0(az ml<\/strong>), but you can also use Python SDK v2.<\/p>\n

\u00a0\"Image<\/a><\/p>\n

Prerequisites<\/h2>\n