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Learn how to deploy your machine learning or deep learning model as a web service in the Azure cloud.
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> [!TIP]
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> Managed online endpoints (preview) provide a way to deploy your trained model without having to create and manage the underlying infrastructure. For more information, see [Deploy and score a machine learning model with a managed online endpoint (preview)](how-to-deploy-managed-online-endpoints.md).
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For more information on the concepts involved in the machine learning deployment workflow, see [Manage, deploy, and monitor models with Azure Machine Learning](concept-model-management-and-deployment.md).
- An Azure Machine Learning workspace. For more information, see [Create an Azure Machine Learning workspace](how-to-manage-workspace.md).
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- A model. If you don't have a trained model, you can use the model and dependency files provided in [this tutorial](https://aka.ms/azml-deploy-cloud).
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- A model. The examples in this article use a pre-trained model.
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- A machine that can run Docker, such as a [compute instance](how-to-create-manage-compute-instance.md).
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# [Python](#tab/python)
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- An Azure Machine Learning workspace. For more information, see [Create an Azure Machine Learning workspace](how-to-manage-workspace.md).
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- A model. If you don't have a trained model, you can use the model and dependency files provided in [this tutorial](https://aka.ms/azml-deploy-cloud).
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- A model. The examples in this article use a pre-trained model.
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- The [Azure Machine Learning software development kit (SDK) for Python](/python/api/overview/azure/ml/intro).
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- A machine that can run Docker, such as a [compute instance](how-to-create-manage-compute-instance.md).
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