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Author: JKirsch1

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

@@ -18,22 +18,22 @@ ms.devlang: azurecli
 
 [!INCLUDE [dev v2](includes/machine-learning-dev-v2.md)]
 
-Learn how to use a custom container to deploy a model to an online endpoint in Azure Machine Learning.
+In Azure Machine Learning, you can use a custom container to deploy a model to an online endpoint.
 
-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.
+Custom container deployments can use web servers other than the default Python Flask server that Azure Machine Learning uses. When you use a custom deployment, you can still take advantage of the built-in monitoring, scaling, alerting, and authentication that Azure Machine Learning offers.
 
-The following table lists various [deployment examples](https://github.com/Azure/azureml-examples/tree/main/cli/endpoints/online/custom-container) that use custom containers such as TensorFlow Serving, TorchServe, Triton Inference Server, Plumber R package, and Azure Machine Learning Inference Minimal image.
+The following table lists various [deployment examples](https://github.com/Azure/azureml-examples/tree/main/cli/endpoints/online/custom-container) that use custom containers. The examples use various tools and technologies, such as TensorFlow Serving, TorchServe, Triton Inference Server, the Plumber R package, and the Azure Machine Learning inference minimal image.
 
 |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 Azure Machine Learning 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 Azure Machine Learning 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 Azure Machine Learning 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 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 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.|
-|[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|
+|[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)|Deploys multiple models to a single deployment by extending the Azure Machine Learning 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)|Deploys a single model by extending the Azure Machine Learning 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)|Deploys two MLFlow models with different Python requirements to two separate deployments behind a single endpoint. Uses the Azure Machine Learning 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)|Deploys three regression models to one endpoint. Uses 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)|Deploys a Half Plus Two model by using a TensorFlow Serving custom container. Uses 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)|Deploys a Half Plus Two model by 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)|Deploys a single model by using a TorchServe custom container.|
+|[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)|Deploys a Triton model by using a custom container.|
 
 This article focuses on serving a TensorFlow model with TensorFlow (TF) Serving.