Updated MLFlow article

Author: luisquintanilla

articles/machine-learning/how-to-deploy-mlflow-models-online-endpoints.md

@@ -13,9 +13,9 @@ ms.reviewer: larryfr
 ms.custom: deploy, mlflow, devplatv2, no-code-deployment
 ---
 
-# Deploy MLflow models to managed online endpoint (preview)
+# Deploy MLflow models to online endpoints (preview)
 
-In this article, learn how to deploy your [MLflow](https://www.mlflow.org) model to a [managed online endpoint](concept-endpoints.md#managed-online-endpoints) (preview). When you deploy your MLflow model to a managed online endpoint, it's a no-code-deployment. It doesn't require scoring script and environment. 
+In this article, learn how to deploy your [MLflow](https://www.mlflow.org) model to an [online endpoint](concept-endpoints.md) (preview). When you deploy your MLflow model to an online endpoint, it's a no-code-deployment. It doesn't require scoring script and environment. 
 
 [!INCLUDE [preview disclaimer](../../includes/machine-learning-preview-generic-disclaimer.md)]
 
@@ -33,7 +33,7 @@ In this code snippets used in this article, the `ENDPOINT_NAME` environment vari
 
 ## Deploy using CLI (v2)
 
-This example shows how you can deploy an MLflow model to managed online endpoint using CLI (v2).
+This example shows how you can deploy an MLflow model to an online endpoint using CLI (v2).
 
 > [!IMPORTANT]
 > For MLflow no-code-deployment, **[testing via local endpoints](how-to-deploy-managed-online-endpoints.md#deploy-and-debug-locally-by-using-local-endpoints)** is currently not supported.
@@ -88,7 +88,7 @@ Once you're done with the endpoint, use the following command to delete it:
 
 ## Deploy using Azure Machine Learning studio
 
-This example shows how you can deploy an MLflow model to managed online endpoint using [Azure Machine Learning studio](https://ml.azure.com).
+This example shows how you can deploy an MLflow model to an online endpoint using [Azure Machine Learning studio](https://ml.azure.com).
 
 1. Register your model in MLflow format using the following YAML and CLI command. The YAML uses a scikit-learn MLflow model from [https://github.com/Azure/azureml-examples/tree/main/cli/endpoints/online/mlflow](https://github.com/Azure/azureml-examples/tree/main/cli/endpoints/online/mlflow).
 
@@ -139,7 +139,7 @@ This example shows how you can deploy an MLflow model to managed online endpoint
 
 ## Deploy models after a training job
 
-This section helps you understand how to deploy models to managed online endpoint once you have completed your [training job](how-to-train-cli.md).
+This section helps you understand how to deploy models to an online endpoint once you have completed your [training job](how-to-train-cli.md).
 
 1. Download the outputs from the training job. The outputs contain the model folder.
 
@@ -168,10 +168,10 @@ This section helps you understand how to deploy models to managed online endpoin
 To learn more, review these articles:
 
 - [Deploy models with REST (preview)](how-to-deploy-with-rest.md)
-- [Create and use managed online endpoints (preview) in the studio](how-to-use-managed-online-endpoint-studio.md)
+- [Create and use online endpoints (preview) in the studio](how-to-use-managed-online-endpoint-studio.md)
 - [Safe rollout for online endpoints (preview)](how-to-safely-rollout-managed-endpoints.md)
 - [How to autoscale managed online endpoints](how-to-autoscale-endpoints.md)
 - [Use batch endpoints (preview) for batch scoring](how-to-use-batch-endpoint.md)
 - [View costs for an Azure Machine Learning managed online endpoint (preview)](how-to-view-online-endpoints-costs.md)
-- [Access Azure resources with a managed online endpoint and managed identity (preview)](how-to-access-resources-from-endpoints-managed-identities.md)
-- [Troubleshoot managed online endpoints deployment](how-to-troubleshoot-managed-online-endpoints.md)
+- [Access Azure resources with an online endpoint and managed identity (preview)](how-to-access-resources-from-endpoints-managed-identities.md)
+- [Troubleshoot online endpoint deployment](how-to-troubleshoot-managed-online-endpoints.md)