Merge pull request #219622 from shohei1029/patch-7

add table for custom container examples

Author: prmerger-automator[bot]

articles/machine-learning/how-to-deploy-custom-container.md

@@ -14,7 +14,7 @@ ms.custom: deploy, devplatv2, devx-track-azurecli, cliv2, event-tier1-build-2022
 ms.devlang: azurecli
 ---
 
-# Deploy a TensorFlow model served with TF Serving using a custom container in an online endpoint
+# Deploy a TensorFlow model served with TensorFlow Serving using a custom container in an online endpoint
 
 [!INCLUDE [cli v2](../../includes/machine-learning-cli-v2.md)]
 
@@ -24,7 +24,21 @@ Learn how to deploy a custom container as an online endpoint in Azure Machine Le
 
 Custom container deployments can use web servers other than the default Python Flask server used by Azure Machine Learning. Users of these deployments can still take advantage of Azure Machine Learning's built-in monitoring, scaling, alerting, and authentication.
 
-This article focuses on serving a TensorFlow model with TensorFlow Serving. You can find [various examples](https://github.com/Azure/azureml-examples/tree/main/cli/endpoints/online/custom-container) for TorchServe, Triton Inference Server, Plumber R package, and AzureML Inference Minimal image.
+You can find [various examples](https://github.com/Azure/azureml-examples/tree/main/cli/endpoints/online/custom-container) for TensorFlow Serving, TorchServe, Triton Inference Server, Plumber R package, and AzureML Inference Minimal image as below:
+
+|Example|Script (CLI)|Description| 
+|-------|------|---------|
+|[minimal/multimodel](https://github.com/Azure/azureml-examples/blob/main/cli/endpoints/online/custom-container/minimal/multimodel)|[deploy-custom-container-minimal-multimodel](https://github.com/Azure/azureml-examples/blob/main/cli/deploy-custom-container-minimal-multimodel.sh)|Deploy multiple models to a single deployment by extending the AzureML Inference Minimal image.|
+|[minimal/single-model](https://github.com/Azure/azureml-examples/blob/main/cli/endpoints/online/custom-container/minimal/single-model)|[deploy-custom-container-minimal-single-model](https://github.com/Azure/azureml-examples/blob/main/cli/deploy-custom-container-minimal-single-model.sh)|Deploy a single model by extending the AzureML Inference Minimal image.|
+|[mlflow/multideployment-scikit](https://github.com/Azure/azureml-examples/blob/main/cli/endpoints/online/custom-container/mlflow/multideployment-scikit)|[deploy-custom-container-mlflow-multideployment-scikit](https://github.com/Azure/azureml-examples/blob/main/cli/deploy-custom-container-mlflow-multideployment-scikit.sh)|Deploy two MLFlow models with different Python requirements to two separate deployments behind a single endpoint using the AzureML Inference Minimal Image.|
+|[r/multimodel-plumber](https://github.com/Azure/azureml-examples/blob/main/cli/endpoints/online/custom-container/r/multimodel-plumber)|[deploy-custom-container-r-multimodel-plumber](https://github.com/Azure/azureml-examples/blob/main/cli/deploy-custom-container-r-multimodel-plumber.sh)|Deploy three regression models to one endpoint using the Plumber R package|
+|[tfserving/half-plus-two](https://github.com/Azure/azureml-examples/blob/main/cli/endpoints/online/custom-container/tfserving/half-plus-two)|[deploy-custom-container-tfserving-half-plus-two](https://github.com/Azure/azureml-examples/blob/main/cli/deploy-custom-container-tfserving-half-plus-two.sh)|Deploy a simple Half Plus Two model using a TensorFlow Serving custom container using the standard model registration process.|
+|[tfserving/half-plus-two-integrated](https://github.com/Azure/azureml-examples/blob/main/cli/endpoints/online/custom-container/tfserving/half-plus-two-integrated)|[deploy-custom-container-tfserving-half-plus-two-integrated](https://github.com/Azure/azureml-examples/blob/main/cli/deploy-custom-container-tfserving-half-plus-two-integrated.sh)|Deploy a simple Half Plus Two model using a TensorFlow Serving custom container with the model integrated into the image.|
+|[torchserve/densenet](https://github.com/Azure/azureml-examples/blob/main/cli/endpoints/online/custom-container/torchserve/densenet)|[deploy-custom-container-torchserve-densenet](https://github.com/Azure/azureml-examples/blob/main/cli/deploy-custom-container-torchserve-densenet.sh)|Deploy a single model using a TorchServe custom container.|
+|[torchserve/huggingface-textgen](https://github.com/Azure/azureml-examples/blob/main/cli/endpoints/online/custom-container/torchserve/huggingface-textgen)|[deploy-custom-container-torchserve-huggingface-textgen](https://github.com/Azure/azureml-examples/blob/main/cli/deploy-custom-container-torchserve-huggingface-textgen.sh)|Deploy Hugging Face models to an online endpoint and follow along with the Hugging Face Transformers TorchServe example.| 
+|[triton/single-model](https://github.com/Azure/azureml-examples/blob/main/cli/endpoints/online/custom-container/triton/single-model)|[deploy-custom-container-triton-single-model](https://github.com/Azure/azureml-examples/blob/main/cli/deploy-custom-container-triton-single-model.sh)|Deploy a Triton model using a custom container|
+
+This article focuses on serving a TensorFlow model with TensorFlow (TF) Serving. 
 
 > [!WARNING]
 > Microsoft may not be able to help troubleshoot problems caused by a custom image. If you encounter problems, you may be asked to use the default image or one of the images Microsoft provides to see if the problem is specific to your image.
@@ -171,7 +185,7 @@ endpoint = ManagedOnlineEndpoint(
 
 ### Configure online deployment
 
-A deployment is a set of resources required for hosting the model that does the actual inferencing. We will create a deployment for our endpoint using the `ManagedOnlineDeployment` class.
+A deployment is a set of resources required for hosting the model that does the actual inferencing. We'll create a deployment for our endpoint using the `ManagedOnlineDeployment` class.
 
 > [!TIP]
 > - `name` - Name of the deployment.
@@ -293,7 +307,7 @@ blue_deployment = ManagedOnlineDeployment(
 
 ---
 
-then your model will be located at `/var/tfserving-model-mount/tfserving-deployment/1` in your deployment. Note that it is no longer under `azureml-app/azureml-models`, but under the mount path you specified:
+then your model will be located at `/var/tfserving-model-mount/tfserving-deployment/1` in your deployment. Note that it's no longer under `azureml-app/azureml-models`, but under the mount path you specified:
 
 :::image type="content" source="./media/how-to-deploy-custom-container/mount-path-deployment-location.png" alt-text="Diagram showing a tree view of the deployment directory structure when using mount_model_path.":::
 
@@ -317,7 +331,7 @@ az ml online-deployment create --name tfserving-deployment -f endpoints/online/c
 
 # [Python SDK](#tab/python)
 
-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.
+Using the `MLClient` created earlier, we'll now create the Endpoint in the workspace. This command will start the endpoint creation and return a confirmation response while the endpoint creation continues.
 
 ```python
 ml_client.begin_create_or_update(endpoint)
@@ -341,12 +355,12 @@ Once your deployment completes, see if you can make a scoring request to the dep
 
 # [Python SDK](#tab/python)
 
-Using the `MLClient` created earlier, we will get a handle to the endpoint. The endpoint can be invoked using the `invoke` command with the following parameters:
+Using the `MLClient` created earlier, we'll get a handle to the endpoint. The endpoint can be invoked using the `invoke` command with the following parameters:
 - `endpoint_name` - Name of the endpoint
 - `request_file` - File with request data
 - `deployment_name` - Name of the specific deployment to test in an endpoint
 
-We will send a sample request using a json file. The sample json is in the [example repository](https://github.com/Azure/azureml-examples/tree/main/sdk/python/endpoints/online/custom-container).
+We'll send a sample request using a json file. The sample json is in the [example repository](https://github.com/Azure/azureml-examples/tree/main/sdk/python/endpoints/online/custom-container).
 
 ```python
 # test the blue deployment with some sample data