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articles/machine-learning/how-to-safely-rollout-online-endpoints.md

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## Prepare your system
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# [Azure CLI](#tab/azure-cli)
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### Set environment variables
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If you haven't already set the defaults for the Azure CLI, save your default settings. To avoid passing in the values for your subscription, workspace, and resource group multiple times, run this code:
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az configure --defaults workspace=<Azure Machine Learning workspace name> group=<resource group>
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```
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# [Azure CLI](#tab/azure-cli)
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### Clone the examples repository
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To follow along with this article, first clone the [examples repository (azureml-examples)](https://github.com/azure/azureml-examples). Then, go to the repository's `cli/` directory:
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You can test mirror traffic by invoking the endpoint several times:
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[!notebook-python[](~/azureml-examples-main/sdk/python/endpoints/online/managed/online-endpoints-safe-rollout.ipynb?name=several_tests_to_mirror_traffic)]
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# [Studio](#tab/azure-studio)
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M.A: add details here
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Mirroring has the following limitations:
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[!notebook-python[](~/azureml-examples-main/sdk/python/endpoints/online/managed/online-endpoints-safe-rollout.ipynb?name=disable_traffic_mirroring)]
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# [Studio](#tab/azure-studio)
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M.A: add details here
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## Test the new deployment with a small percentage of live traffic

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