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Merge pull request #110911 from anthonychu/20200409-add-byos-tensorflow-tutorial
Azure Functions - Add bring your own storage to TensorFlow tutorial
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articles/azure-functions/functions-machine-learning-tensorflow.md

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@@ -149,8 +149,10 @@ In Azure Functions, a function project is a container for one or more individual
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To modify the `classify` function to classify an image based on its contents, you use a pre-built TensorFlow model that was trained with and exported from Azure Custom Vision Service. The model, which is contained in the *resources* folder of the sample you cloned earlier, classifies an image based on whether it contains a dog or a cat. You then add some helper code and dependencies to your project.
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To build your own model using the free tier of the Custom Vision Service, follow the instructions in the [sample project repository](https://github.com/Azure-Samples/functions-python-tensorflow-tutorial/blob/master/train-custom-vision-model.md).
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> [!TIP]
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> If you want to build your own model using the free tier of the Custom Vision Service, follow the instructions in the [sample project repository](https://github.com/Azure-Samples/functions-python-tensorflow-tutorial/blob/master/train-custom-vision-model.md).
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> If you want to host your TensorFlow model independent of the function app, you can instead mount a file share containing your model to your Linux function app. To learn more, see [Mount a file share to a Python function app using Azure CLI](./scripts/functions-cli-mount-files-storage-linux.md).
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1. In the *start* folder, run following command to copy the model files into the *classify* folder. Be sure to include `\*` in the command.
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@@ -311,3 +313,4 @@ See also:
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- [Deploy the function to Azure using Visual Studio Code](https://code.visualstudio.com/docs/python/tutorial-azure-functions).
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- [Azure Functions Python Developer Guide](./functions-reference-python.md)
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- [Mount a file share to a Python function app using Azure CLI](./scripts/functions-cli-mount-files-storage-linux.md)

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