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Fix Python/CLI tabs
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‎articles/machine-learning/concept-azure-machine-learning-v2.md

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@@ -40,7 +40,7 @@ The workspace is the top-level resource for Azure Machine Learning, providing a
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### Create a workspace
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### [CLI](#tab/cli)
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### [Azure CLI](#tab/cli)
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To create a workspace using CLI v2, use the following command:
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* **Inference cluster** - used to deploy trained machine learning models to Azure Kubernetes Service. You can create an Azure Kubernetes Service (AKS) cluster from your Azure ML workspace, or attach an existing AKS cluster.
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* **Attached compute** - You can attach your own compute resources to your workspace and use them for training and inference.
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### [CLI](#tab/cli)
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### [Azure CLI](#tab/cli)
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To create a compute using CLI v2, use the following command:
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* Azure Data Lake
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* Azure Data Lake Gen2
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### [CLI](#tab/cli)
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### [Azure CLI](#tab/cli)
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To create a datastore using CLI v2, use the following command:
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### Creating a model
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### [CLI](#tab/cli)
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### [Azure CLI](#tab/cli)
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To create a model using CLI v2, use the following command:
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### Create an Azure ML custom environment
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### [CLI](#tab/cli)
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### [Azure CLI](#tab/cli)
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To create an environment using CLI v2, use the following command:
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‎articles/machine-learning/how-to-attach-kubernetes-to-workspace.md

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Azure Relay resource is created during the extension deployment under the same Resource Group as the Arc-enabled Kubernetes cluster.
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### [CLI](#tab/cli)
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### [Azure CLI](#tab/cli)
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[!INCLUDE [cli v2](../../includes/machine-learning-cli-v2.md)]
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> `--user-assigned-identities` is only required for `UserAssigned` managed identities. Although you can provide a list of comma-separated user managed identities, only the first one is used when you attach your cluster.
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>
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> Compute attach won't create the Kubernetes namespace automatically or validate whether the kubernetes namespace existed. You need to verify that the specified namespace exists in your cluster, otherwise, any AzureML workloads submitted to this compute will fail.
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### [Python](#tab/python)
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### [Python SDK](#tab/python)
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[!INCLUDE [sdk v1](../../includes/machine-learning-sdk-v1.md)]
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‎articles/machine-learning/how-to-create-data-assets.md

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Below shows you how to create a *folder* as an asset:
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# [CLI](#tab/CLI)
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# [Azure CLI](#tab/cli)
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Create a `YAML` file (`<file-name>.yml`):
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az ml data create -f <file-name>.yml
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```
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# [Python-SDK](#tab/Python-SDK)
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# [Python SDK](#tab/Python-SDK)
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You can create a data asset in Azure Machine Learning using the following Python Code:
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Below shows you how to create a *specific file* as a data asset:
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# [CLI](#tab/CLI)
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# [Azure CLI](#tab/cli)
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Sample `YAML` file `<file-name>.yml` for data in local path is as below:
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> az ml data create -f <file-name>.yml
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```
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# [Python-SDK](#tab/Python-SDK)
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# [Python SDK](#tab/Python-SDK)
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```python
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from azure.ai.ml.entities import Data
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from azure.ai.ml.constants import AssetTypes
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Below shows you how to create an `mltable` data asset. The `path` can be any of the supported path formats outlined above.
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# [CLI](#tab/CLI)
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# [Azure CLI](#tab/cli)
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Create a `YAML` file (`<file-name>.yml`):
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az ml data create -f <file-name>.yml
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```
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# [Python-SDK](#tab/Python-SDK)
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# [Python SDK](#tab/Python-SDK)
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You can create a data asset in Azure Machine Learning using the following Python Code:
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‎articles/machine-learning/how-to-deploy-automl-endpoint.md

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1. Complete all the steps in wizard to create an online endpoint and deployment
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# [Azure CLI](#tab/cli)
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[!INCLUDE [cli v2](../../includes/machine-learning-cli-v2.md)]
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After you create a deployment, you can score it as described in [Invoke the endpoint to score data by using your model](how-to-deploy-managed-online-endpoints.md#invoke-the-endpoint-to-score-data-by-using-your-model).
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# [Python](#tab/python)
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# [Python SDK](#tab/python)
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[!INCLUDE [sdk v2](../../includes/machine-learning-sdk-v2.md)]
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‎articles/machine-learning/how-to-deploy-custom-container.md

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* To deploy locally, you must have [Docker engine](https://docs.docker.com/engine/install/) running locally. This step is **highly recommended**. It will help you debug issues.
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# [CLI](#tab/CLI)
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# [Azure CLI](#tab/cli)
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* Install and configure the Azure CLI and ML extension. For more information, see [Install, set up, and use the CLI (v2)](how-to-configure-cli.md).
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az configure --defaults workspace=<azureml workspace name> group=<resource group>
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```
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# [Python](#tab/python)
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# [Python SDK](#tab/python)
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* If you haven't installed Python SDK v2, please install with this command:
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To follow along with this tutorial, download the source code below.
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# [CLI](#tab/CLI)
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# [Azure CLI](#tab/cli)
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```azurecli
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git clone https://github.com/Azure/azureml-examples --depth 1
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cd azureml-examples/cli
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```
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# [Python](#tab/python)
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# [Python SDK](#tab/python)
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```azurecli
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git clone https://github.com/Azure/azureml-examples --depth 1
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## Deploy your online endpoint to Azure
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Next, deploy your online endpoint to Azure.
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# [CLI](#tab/CLI)
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# [Azure CLI](#tab/cli)
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### Create a YAML file for your endpoint and deployment
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:::code language="yaml" source="~/azureml-examples-main/cli/endpoints/online/custom-container/tfserving-deployment.yml":::
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# [Python](#tab/python)
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# [Python SDK](#tab/python)
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### Connect to Azure Machine Learning workspace
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Connect to Azure Machine Learning Workspace, configure workspace details, and get a handle to the workspace as follows:
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:::image type="content" source="./media/how-to-deploy-custom-container/local-directory-structure.png" alt-text="Diagram showing a tree view of the local directory structure.":::
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and `tfserving-deployment.yml` contains:
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path: ./half_plus_two
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```
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# [Python SDK](#tab/python)
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> [!IMPORTANT]
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> The `model_mount_path` must be a valid absolute path in Linux (the OS of the container image).
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# [Azure CLI](#tab/cli)
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For example, you can have `model_mount_path` parameter in your _tfserving-deployment.yml_:
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```
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For example, you can have `model_mount_path` parameter in your `ManagedOnlineDeployment` class:
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### Create your endpoint and deployment
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Now that you've understood how the YAML was constructed, create your endpoint.
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Using the `MLClient` created earlier, we will now create the Endpoint in the workspace. This command will start the endpoint creation and return a confirmation response while the endpoint creation continues.
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Once your deployment completes, see if you can make a scoring request to the deployed endpoint.
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:::code language="azurecli" source="~/azureml-examples-main/cli/deploy-tfserving.sh" id="invoke_endpoint":::
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```azurecli
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‎articles/machine-learning/how-to-read-write-data-v2.md

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## Read data in a job
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from azure.ai.ml import command

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