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

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## Model
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Azure machine learning models consist of the binary file(s) that represent a machine learning model and any corresponding metadata. Models can be created from a local or remote file or directory. For remote locations `https`, `wasbs` and `azureml` locations are supported. The created model will be tracked in the workspace under the specified name and version. Azure Machine Learning supports three types of storage format for models:
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Azure Machine Learning models consist of the binary file(s) that represent a machine learning model and any corresponding metadata. Models can be created from a local or remote file or directory. For remote locations `https`, `wasbs` and `azureml` locations are supported. The created model will be tracked in the workspace under the specified name and version. Azure Machine Learning supports three types of storage format for models:
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* `custom_model`
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* `mlflow_model`
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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.md?tabs=python#create-an-environment).
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To create an environment using Python SDK v2, see [Create an environment](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.md?tabs=cli#create-an-environment).
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To create an environment using CLI v2, see [Create an environment](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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