touchups

Author: v-thepet

articles/machine-learning/concept-model-management-and-deployment.md

@@ -1,7 +1,7 @@
 ---
 title: MLOps machine learning model management
 titleSuffix: Azure Machine Learning
-description: Learn how to deploy, manage, track lineage, and monitor your models by using machine learning operations (MLOps) with Azure Machine Learning.
+description: Learn how Azure Machine Learning uses machine learning operations (MLOps) to help manage the lifecycle of your models.
 services: machine-learning
 ms.service: azure-machine-learning
 ms.subservice: mlops
@@ -25,7 +25,7 @@ MLOps is based on [DevOps](https://azure.microsoft.com/overview/what-is-devops/)
 - Faster deployment of models into production.
 - Better quality assurance and end-to-end lineage tracking.
 
-Use MLOps in the machine learning process to provide the following capabilities:
+MLOps provides the following capabilities to the machine learning process:
 
 - **Create reproducible machine learning pipelines** to define repeatable and reusable steps for data preparation, training, and scoring processes.
 - **Create reusable software environments** for training and deploying models.
@@ -47,13 +47,13 @@ For more information on Azure Machine Learning pipelines, see [Machine learning
 
 ## Reusable software environments
 
-Azure Machine Learning environments ensure that builds are reproducible without using manual software configurations. Use environments to track and reproduce the pip and conda software dependencies for your projects.
+Azure Machine Learning environments ensure that builds are reproducible without using manual software configurations. Environments can track and reproduce the pip and conda software dependencies for your projects.
 
 You can use environments for model training and deployment. For more information on environments, see [Azure Machine Learning environments](concept-environments.md).
 
 ## Model registration, packaging, and deployment
 
-Azure Machine Learning can use MLOps to register, package, and deploy models from anywhere.
+Azure Machine Learning can use MLOps from anywhere to register, package, and deploy models.
 
 ### Register and track models
 
@@ -84,11 +84,11 @@ You can convert your model to [Open Neural Network Exchange](https://onnx.ai) (O
 
 For more information on ONNX with Azure Machine Learning, see [Create and accelerate machine learning models](concept-onnx.md).
 
-### Deploy models
+### Deploy models as endpoints
 
-You can deploy trained machine learning models as [endpoints](concept-endpoints.md) locally or in the cloud. Deployments use CPU and GPU for inferencing.
+You can deploy trained machine learning models as [endpoints](concept-endpoints.md) locally or in the cloud. Deployments use CPUs and GPUs for inferencing.
 
-To deploy a model to an endpoint, you need to provide the following information:
+To deploy a model as an endpoint, you need to provide the following information:
 
 - The **model** used to score data submitted to the service or device.
 - An **entry script**, also called **scoring script**, that accepts requests, uses the models to score the data, and returns a response.
@@ -98,20 +98,20 @@ To deploy a model to an endpoint, you need to provide the following information:
 You also provide the configuration of the target deployment platform, such as the virtual machine (VM) family type, available memory, and number of cores. When Azure Machine Learning creates the image, it also adds any components it needs, such as assets needed to run the web service.
 
 >[!IMPORTANT]
->When you deploy an MLflow model, you don't need to provide an entry script or an environment for the deployment. For more information on deploying MLflow models, see [Guidelines for deploying MLflow models](how-to-deploy-mlflow-models.md).
+>When you deploy an [MLflow model](concept-mlflow.md), you don't need to provide an entry script or an environment for the deployment. For more information on deploying MLflow models, see [Guidelines for deploying MLflow models](how-to-deploy-mlflow-models.md).
 
 #### Batch scoring and endpoints
 
 Batch scoring is supported through batch endpoints. For more information on batch scoring, see [Batch endpoints](concept-endpoints-batch.md).
 
 #### Real-time scoring with online endpoints
 
-You can use your models with online endpoints for real-time scoring. Compute targets for online endpoints can be local endpoints, managed online endpoints, or Azure Kubernetes Service (AKS) endpoints.
+You can use your models with [online endpoints](concept-endpoints-online.md) for real-time scoring. Compute targets for online endpoints can be local endpoints, managed online endpoints, or Azure Kubernetes Service (AKS) endpoints.
 
 To deploy a model to an online endpoint, you need to provide the following information:
 
 - The model or ensemble of models.
-- Dependencies required to use the model, for example a script that accepts requests and invokes the model and conda dependencies.
+- Dependencies required to use the model, for example, a script that accepts requests and invokes the model and conda dependencies.
 - Deployment configuration that describes how and where to deploy the model.
 
 For more information on deployment for real-time scoring, see [Deploy online endpoints](how-to-deploy-online-endpoints.md).
@@ -126,21 +126,17 @@ When you deploy to an online endpoint, you can use controlled rollout to enable
 
 For more information on deployment using a controlled rollout, see [Perform safe rollout of new deployments for real-time inference](how-to-safely-rollout-online-endpoints.md).
 
-### Analytics
-
-Microsoft Power BI supports using machine learning models for data analytics. For more information, see [Azure Machine Learning integration in Power BI](/power-bi/transform-model/dataflows/dataflows-machine-learning-integration).
-
 ## Metadata for machine learning lifecycle governance
 
 Azure Machine Learning gives you the capability to track the end-to-end audit trail of all your machine learning assets by using metadata. For example:
 
 - [Azure Machine Learning data assets](how-to-create-register-datasets.md) help you track, profile, and version data.
 - [Model interpretability](how-to-machine-learning-interpretability.md) allows you to explain your models, meet regulatory compliance, and understand how models arrive at a result for a given input.
 - Azure Machine Learning job history stores a snapshot of the code, data, and computes used to train a model.
-- The [Azure Machine Learning model registry](how-to-manage-models.md?tabs=use-local#create-a-model-in-the-model-registry) captures all the metadata associated with your model. For example, which experiment trained the model, where the model is being deployed, and whether the model deployments are healthy.
+- [Azure Machine Learning model registration](how-to-manage-models.md?tabs=use-local#create-a-model-in-the-model-registry) captures all the metadata associated with your model. For example, which experiment trained the model, where the model is being deployed, and whether the model deployments are healthy.
 - [Integration with Azure](how-to-use-event-grid.md) lets you act on events in the machine learning lifecycle, such as model registration, deployment, data drift, and training job events.
 
-Some information on models and data assets is automatically captured, but you can add more information by using *tags*. When you look for registered models and data assets in your workspace, you can use tags as a filter.
+Some information on models and data assets is automatically captured, but you can add more information by using *tags*. When you look for registered models and data assets in your workspace, you can use tags as filters.
 
 >[!NOTE]
 >When you use the **Tags** in the **Filter by** option on the **Models** page of Azure Machine Learning studio, be sure to use `TagName=TagValue` without spaces rather than `TagName : TagValue`.
@@ -151,7 +147,7 @@ Azure Machine Learning publishes key events to Azure Event Grid, which can be us
 
 ## Machine learning lifecycle automation
 
-You can use Git and [Azure Pipelines](/azure/devops/pipelines/get-started/what-is-azure-pipelines) to create a continuous integration process that trains a machine learning model. In a typical scenario, when a data scientist checks a change into a project's Git repository, Azure Pipelines starts a training job.
+You can use Git and [Azure Pipelines](/azure/devops/pipelines/get-started/what-is-azure-pipelines) to create a continuous integration process that trains a machine learning model. In a typical scenario, when a data scientist checks a change into a project's Git repository, Azure Pipelines starts the training job.
 
 You can inspect the job results to see the performance characteristics of the trained model. You can also create a pipeline that deploys the model as a web service.
 
@@ -162,6 +158,10 @@ The [Machine Learning extension](https://marketplace.visualstudio.com/items?item
 
 For more information on using Azure Pipelines with Azure Machine Learning, see [Use Azure Pipelines with Azure Machine Learning](how-to-devops-machine-learning.md).
 
+## Analytics
+
+Microsoft Power BI supports using machine learning models for data analytics. For more information, see [AI with dataflows](/power-bi/transform-model/dataflows/dataflows-machine-learning-integration).
+
 ## Related content
 
 - [Set up MLOps with Azure DevOps](how-to-setup-mlops-azureml.md)