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articles/machine-learning/concept-azure-machine-learning-v2.md

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### [Azure CLI](#tab/cli)
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To use the Azure CLI code examples in this article, you need to have the Azure CLI installed and configured. You can install the Azure CLI from the [Azure CLI documentation](https://docs.microsoft.com/cli/azure/install-azure-cli).
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To use the Azure CLI code examples in this article, you need to have the Azure CLI installed and configured. You can install the Azure CLI from the [Azure CLI documentation](/azure/install-azure-cli).
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### [Studio](#tab/azure-studio)
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Model registration allows you to store and version your models in the Azure cloud, in your workspace. The model registry helps you organize and keep track of your trained models.
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For more information on how to create models in the registry, see [Work with models in Azure Machine Learning](how-to-manage-models).
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For more information on how to create models in the registry, see [Work with models in Azure Machine Learning](how-to-manage-models.md).
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## Environment
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### [Python SDK](#tab/sdk)
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To create an environment using Python SDK v2, see [Create an envirnment](how-to-manage-environments-v2&tabs=python#create-an-environment).
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To create an environment using Python SDK v2, see [Create an envirnment](how-to-manage-environments-v2.md&tabs=python#create-an-environment).
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This [Jupyter notebook](https://github.com/Azure/azureml-examples/blob/main/sdk/python/assets/environment/environment.ipynb) shows more ways to create custom environments using SDK v2.
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### [Azure CLI](#tab/cli)
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To create an environment using CLI v2, see [Create an envirnment](how-to-manage-environments-v2&tabs=cli#create-an-environment).
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To create an environment using CLI v2, see [Create an envirnment](how-to-manage-environments-v2.md&tabs=cli#create-an-environment).
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For more information, see [environment YAML schema](reference-yaml-environment.md).
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