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Copy file name to clipboardExpand all lines: articles/machine-learning/service/quickstart-run-cloud-notebook.md
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# Quickstart: Use a cloud-based notebook server to get started with Azure Machine Learning
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In this quickstart, you run Python code from a cloud-based Jupyter notebook that logs values in the [Azure Machine Learning service workspace](concept-azure-machine-learning-architecture.md). The workspace is the foundational block in the cloud that you use to experiment, train, and deploy machine learning models with Machine Learning.
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No install required. Get started with Azure Machine Learning service using a managed notebook server in the cloud. If you want to instead install the SDK into your own Python environment, see [Quickstart: Use your own notebook server to get started with Azure Machine Learning](quickstart-run-local-notebook.md).
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This quickstart shows how to create a cloud virtual machine in your Azure Machine Learning workspace, configured with the Python environment necessary to run Azure Machine Learning. The [notebook VM (Preview)](how-to-configure-environment.md#notebookvm) is a secure, cloud-based Azure workstation that provides data scientists with a Jupyter notebook server, JupyterLab, and a fully prepared ML environment. If you prefer to work locally, you can also [use your own notebook server](quickstart-run-local-notebook.md).
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This quickstart shows how you can use the [Azure Machine Learning service workspace](concept-azure-machine-learning-architecture.md) to keep track of your machine learning experiments. You will create a [notebook VM (Preview)](how-to-configure-environment.md#notebookvm), a secure, cloud-based Azure workstation that provides a Jupyter notebook server, JupyterLab, and a fully prepared ML environment. You then run a Python notebook on this VM that log values into the workspace.
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In this quickstart, you take the following actions:
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* Create a new cloud-based notebook server in your workspace.
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* Create a workspace
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* Create a notebook VM in your workspace.
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* Launch the Jupyter web interface.
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* Open a notebook that contains code to estimate pi and logs errors at each iteration.
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* Run the notebook.
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## Create a workspace
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If you have an Azure Machine Learning service workspace, skip to the [next section](#create-a-cloud-based-notebook-server). Otherwise, create one now.
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If you have an Azure Machine Learning service workspace, skip to the [next section](#create-notebook). Otherwise, create one now.
## <aname="create-notebook"></a>Create a notebook VM
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From your workspace, you create a cloud resource to get started using Jupyter notebooks. This resource gives you a cloud-based platform pre-configured with everything you need to run Azure Machine Learning service.
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In this quickstart, you completed these tasks:
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* Create a workspace
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* Create a notebook VM.
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* Launch the Jupyter web interface.
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* Open a notebook that contains code to estimate pi and logs errors at each iteration.
Copy file name to clipboardExpand all lines: articles/machine-learning/service/quickstart-run-local-notebook.md
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# Quickstart: Use your own notebook server to get started with Azure Machine Learning
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Use your own notebook server to run code that logs values in the [Azure Machine Learning service workspace](concept-azure-machine-learning-architecture.md). The workspace is the foundational block in the cloud that you use to experiment, train, and deploy machine learning models with Machine Learning.
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Use your own Python environment and Jupyter Notebook Server to get started with Azure Machine Learning service. For a quickstart with no SDK installation, see [Quickstart: Use a cloud-based notebook server to get started with Azure Machine Learning](quickstart-run-cloud-notebook.md).
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This quickstart uses your own Python environment and Jupyter Notebook Server. For a quickstart with no SDK installation, see [Quickstart: Use a cloud-based notebook server to get started with Azure Machine Learning](quickstart-run-cloud-notebook.md)
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This quickstart shows how you can use the [Azure Machine Learning service workspace](concept-azure-machine-learning-architecture.md) to keep track of your machine learning experiments. You will run Python code that log values into the workspace.
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