Merge pull request #206827 from Blackmist/note

Moving to v1

Author: Court72

articles/machine-learning/.openpublishing.redirection.machine-learning.json

@@ -1371,9 +1371,9 @@
         "redirect_document_id": true
     },
     {
-        "source_path_from_root": "/articles/machine-learning/service/how-to-enable-app-insights.md",
-        "redirect_url": "/azure/machine-learning/how-to-enable-app-insights",
-        "redirect_document_id": true
+        "source_path_from_root": "/articles/machine-learning/how-to-enable-app-insights.md",
+        "redirect_url": "/azure/machine-learning/how-to-monitor-online-endpoints",
+        "redirect_document_id": false
     },
     {
         "source_path_from_root": "/articles/machine-learning/service/how-to-designer-sample-text-classification.md",

articles/machine-learning/concept-workspace.md

@@ -125,7 +125,7 @@ When you create a new workspace, it automatically creates several Azure resource
     > [!NOTE]
     > If your subscription setting requires adding tags to resources under it, Azure Container Registry (ACR) created by Azure Machine Learning will fail, since we cannot set tags to ACR.
 
-+ [Azure Application Insights](https://azure.microsoft.com/services/application-insights/): Stores monitoring and diagnostics information. For more information, see [Monitor and collect data from Machine Learning web service endpoints](../../articles/machine-learning/how-to-enable-app-insights.md).
++ [Azure Application Insights](https://azure.microsoft.com/services/application-insights/): Stores monitoring and diagnostics information. For more information, see [Monitor online endpoints](how-to-monitor-online-endpoints.md).
 
     > [!NOTE]
     > You can delete the Application Insights instance after cluster creation if you want. Deleting it limits the information gathered from the workspace, and may make it more difficult to troubleshoot problems. __If you delete the Application Insights instance created by the workspace, you cannot re-create it without deleting and recreating the workspace__.

articles/machine-learning/how-to-debug-pipelines.md

@@ -233,7 +233,7 @@ For pipelines created in the designer, you can find the **70_driver_log** file i
 
 ### Enable logging for real-time endpoints
 
-In order to troubleshoot and debug real-time endpoints in the designer, you must enable Application Insight logging using the SDK. Logging lets you troubleshoot and debug model deployment and usage issues. For more information, see [Logging for deployed models](./how-to-enable-app-insights.md). 
+In order to troubleshoot and debug real-time endpoints in the designer, you must enable Application Insight logging using the SDK. Logging lets you troubleshoot and debug model deployment and usage issues. For more information, see [Logging for deployed models](./v1/how-to-enable-app-insights.md). 
 
 ### Get logs from the authoring page
 

articles/machine-learning/how-to-deploy-local-container-notebook-vm.md

@@ -94,5 +94,5 @@ print("prediction:", resp.text)
 * [Deployment troubleshooting](how-to-troubleshoot-deployment.md)
 * [Use TLS to secure a web service through Azure Machine Learning](how-to-secure-web-service.md)
 * [Consume a ML Model deployed as a web service](how-to-consume-web-service.md)
-* [Monitor your Azure Machine Learning models with Application Insights](how-to-enable-app-insights.md)
+* [Monitor your Azure Machine Learning models with Application Insights](./v1/how-to-enable-app-insights.md)
 * [Collect data for models in production](how-to-enable-data-collection.md)

articles/machine-learning/how-to-track-monitor-analyze-runs.md

@@ -34,7 +34,7 @@ This article shows how to do the following tasks:
 > * If you're looking for information on monitoring training jobs from the CLI or SDK v2, see [Track experiments with MLflow and CLI v2](how-to-use-mlflow-cli-runs.md).
 > * If you're looking for information on monitoring the Azure Machine Learning service and associated Azure services, see [How to monitor Azure Machine Learning](monitor-azure-machine-learning.md).
 >
-> If you're looking for information on monitoring models deployed as web services, see [Collect model data](how-to-enable-data-collection.md) and [Monitor with Application Insights](how-to-enable-app-insights.md).
+> If you're looking for information on monitoring models deployed to online endpoints, see [Monitor online endpoints](how-to-monitor-online-endpoints.md).
 
 ## Prerequisites
 

articles/machine-learning/monitor-azure-machine-learning.md

@@ -24,7 +24,7 @@ When you have critical applications and business processes relying on Azure reso
 > * [Track experiments with MLflow](how-to-use-mlflow.md)
 > * [Visualize runs with TensorBoard](how-to-monitor-tensorboard.md)
 >
-> If you want to monitor information generated by models deployed as web services, see [Collect model data](how-to-enable-data-collection.md) and [Monitor with Application Insights](how-to-enable-app-insights.md).
+> If you want to monitor information generated by models deployed to online endpoints, see [Monitor online endpoints](how-to-monitor-online-endpoints.md).
 
 ## What is Azure Monitor?
 
@@ -69,7 +69,7 @@ You can configure the following logs for Azure Machine Learning:
 | AmlComputeClusterEvent | Events from Azure Machine Learning compute clusters. |
 | AmlComputeClusterNodeEvent (deprecated) | Events from nodes within an Azure Machine Learning compute cluster. |
 | AmlComputeJobEvent | Events from jobs running on Azure Machine Learning compute. |
-| AmlComputeCpuGpuUtilization | ML services compute CPU and GPU utilizaion logs. |
+| AmlComputeCpuGpuUtilization | ML services compute CPU and GPU utilization logs. |
 | AmlRunStatusChangedEvent | ML run status changes. |
 | ModelsChangeEvent | Events when ML model is accessed created or deleted. |
 | ModelsReadEvent | Events when ML model is read. |
@@ -141,7 +141,7 @@ Data in Azure Monitor Logs is stored in tables, with each table having its own s
 | AmlDataStoreEvent | Events when ML datastore is accessed (read, created, or deleted). Category includes:DataStoreReadEvent,DataStoreChangeEvent. |
 | AmlDeploymentEvent | Events when a model deployment happens on ACI or AKS. Category includes:DeploymentReadEvent,DeploymentEventACI,DeploymentEventAKS. |
 | AmlInferencingEvent | Events for inference or related operation on AKS or ACI compute type. Category includes:InferencingOperationACI (very chatty),InferencingOperationAKS (very chatty). |
-| AmlModelsEvent | Events when ML model is accessed (read, created, or deleted). Includes events when packaging of models and assets happen into a ready-to-build packages. Category includes:ModelsReadEvent,ModelsActionEvent .|
+| AmlModelsEvent | Events when ML model is accessed (read, created, or deleted). Includes events when packaging of models and assets happen into ready-to-build packages. Category includes:ModelsReadEvent,ModelsActionEvent .|
 | AmlPipelineEvent | Events when ML pipeline draft or endpoint or module are accessed (read, created, or deleted).Category includes:PipelineReadEvent,PipelineChangeEvent. |
 | AmlRunEvent | Events when ML experiments are accessed (read, created, or deleted). Category includes:RunReadEvent,RunEvent. |
 | AmlEnvironmentEvent | Events when ML environment configurations (read, created, or deleted). Category includes:EnvironmentReadEvent (very chatty),EnvironmentChangeEvent. |

articles/machine-learning/tutorial-convert-ml-experiment-to-production.md

@@ -526,4 +526,4 @@ Now that you understand how to convert from an experiment to production code, se
 
 + [MLOpsPython](https://github.com/microsoft/MLOpsPython/blob/master/docs/custom_model.md): Build a CI/CD pipeline to train, evaluate and deploy your own model using Azure Pipelines and Azure Machine Learning
 + [Monitor Azure ML experiment jobs and metrics](./how-to-log-view-metrics.md)
-+ [Monitor and collect data from ML web service endpoints](./how-to-enable-app-insights.md)
++ [Monitor and collect data from ML web service endpoints](./v1/how-to-enable-app-insights.md)

articles/machine-learning/tutorial-train-deploy-notebook.md

@@ -232,11 +232,13 @@ with mlflow.start_run() as run:
 
 ## View experiment
 
-In the left-hand menu in Azure Machine Learning Studio, select __Experiments__ and then select your experiment (__azure-ml-in10-mins-tutorial__). An experiment is a grouping of many runs from a specified script or piece of code. Information for the run is stored under that experiment. If the name doesn't exist when you submit an experiment, if you select your run you will see various tabs containing metrics, logs, explanations, etc.
+In the left-hand menu in Azure Machine Learning studio, select __Jobs__ and then select your job (__azure-ml-in10-mins-tutorial__). A job is a grouping of many runs from a specified script or piece of code.  Multiple jobs can be grouped together as an experiment.
+
+Information for the run is stored under that job. If the name doesn't exist when you submit a job, if you select your run you will see various tabs containing metrics, logs, explanations, etc.
 
 ## Version control your models with the model registry
 
-You can use model registration to store and version your models in your workspace. Registered models are identified by name and version. Each time you register a model with the same name as an existing one, the registry increments the version. The code below registers and versions the model you trained above. Once you have executed the code cell below you will be able to see the model in the registry by selecting __Models__ in the left-hand menu in Azure Machine Learning Studio.
+You can use model registration to store and version your models in your workspace. Registered models are identified by name and version. Each time you register a model with the same name as an existing one, the registry increments the version. The code below registers and versions the model you trained above. Once you have executed the code cell below you will be able to see the model in the registry by selecting __Models__ in the left-hand menu in Azure Machine Learning studio.
 
 ```python
 # register the model
@@ -317,7 +319,7 @@ The scoring script file referenced in the code above can be found in the same fo
 
 ### View endpoint
 
-Once the model has been successfully deployed, you can view the endpoint by navigating to __Endpoints__ in the left-hand menu in Azure Machine Learning Studio. You will be able to see the state of the endpoint (healthy/unhealthy), logs, and consume (how applications can consume the model).
+Once the model has been successfully deployed, you can view the endpoint by navigating to __Endpoints__ in the left-hand menu in Azure Machine Learning studio. You will be able to see the state of the endpoint (healthy/unhealthy), logs, and consume (how applications can consume the model).
 
 ## Test the model service
 
@@ -364,5 +366,5 @@ Use these steps to delete your Azure Machine Learning workspace and all compute
 + Learn about all of the [deployment options for Azure Machine Learning](how-to-deploy-and-where.md).
 + Learn how to [create clients for the web service](how-to-consume-web-service.md).
 + [Make predictions on large quantities of data](./tutorial-pipeline-batch-scoring-classification.md) asynchronously.
-+ Monitor your Azure Machine Learning models with [Application Insights](how-to-enable-app-insights.md).
++ Monitor your Azure Machine Learning models with [Application Insights](./v1/how-to-enable-app-insights.md).
 + Try out the [automatic algorithm selection](tutorial-auto-train-models.md) tutorial.

articles/machine-learning/how-to-enable-app-insights.md renamed to articles/machine-learning/v1/how-to-enable-app-insights.md

@@ -14,9 +14,9 @@ ms.custom: devx-track-python, data4ml, sdkv1, event-tier1-build-2022
 
 # Monitor and collect data from ML web service endpoints
 
-[!INCLUDE [sdk v1](../../includes/machine-learning-sdk-v1.md)]
+[!INCLUDE [sdk v1](../../../includes/machine-learning-sdk-v1.md)]
 
-In this article, you learn how to collect data from models deployed to web service endpoints in Azure Kubernetes Service (AKS) or Azure Container Instances (ACI). Use [Azure Application Insights](../azure-monitor/app/app-insights-overview.md) to collect the following data from an endpoint:
+In this article, you learn how to collect data from models deployed to web service endpoints in Azure Kubernetes Service (AKS) or Azure Container Instances (ACI). Use [Azure Application Insights](../../azure-monitor/app/app-insights-overview.md) to collect the following data from an endpoint:
 * Output data
 * Responses
 * Request rates, response times, and failure rates
@@ -25,18 +25,21 @@ In this article, you learn how to collect data from models deployed to web servi
 
 The [enable-app-insights-in-production-service.ipynb](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.ipynb) notebook demonstrates concepts in this article.
 
-[!INCLUDE [aml-clone-in-azure-notebook](../../includes/aml-clone-for-examples.md)]
+[!INCLUDE [aml-clone-in-azure-notebook](../../../includes/aml-clone-for-examples.md)]
 
 > [!IMPORTANT]
 > The information in this article relies on the Azure Application Insights instance that was created with your workspace. If you deleted this Application Insights instance, there is no way to re-create it other than deleting and recreating the workspace.
+
+> [!TIP]
+> If you are using online endpoints instead, use the information in the [Monitor online endpoints](../how-to-monitor-online-endpoints.md) article instead. 
  
 ## Prerequisites
 
 * An Azure subscription - try the [free or paid version of Azure Machine Learning](https://azure.microsoft.com/free/).
 
-* An Azure Machine Learning workspace, a local directory that contains your scripts, and the Azure Machine Learning SDK for Python installed. To learn more, see [How to configure a development environment](how-to-configure-environment.md).
+* An Azure Machine Learning workspace, a local directory that contains your scripts, and the Azure Machine Learning SDK for Python installed. To learn more, see [How to configure a development environment](../how-to-configure-environment.md).
 
-* A trained machine learning model. To learn more, see the [Train image classification model](tutorial-train-deploy-notebook.md) tutorial.
+* A trained machine learning model. To learn more, see the [Train image classification model](../tutorial-train-deploy-notebook.md) tutorial.
 
 <a name="python"></a>
 
@@ -63,7 +66,7 @@ Use the following steps to update an existing web service:
 ### Log custom traces in your service
 
 > [!IMPORTANT]
-> Azure Application Insights only logs payloads of up to 64kb. If this limit is reached, you may see errors such as out of memory, or no information may be logged. If the data you want to log is larger 64kb, you should instead store it to blob storage using the information in [Collect Data for models in production](how-to-enable-data-collection.md).
+> Azure Application Insights only logs payloads of up to 64kb. If this limit is reached, you may see errors such as out of memory, or no information may be logged. If the data you want to log is larger 64kb, you should instead store it to blob storage using the information in [Collect Data for models in production](../how-to-enable-data-collection.md).
 >
 > For more complex situations, like model tracking within an AKS deployment, we recommend using a third-party library like [OpenCensus](https://opencensus.io).
 
@@ -200,7 +203,7 @@ Azure Application Insights stores your service logs in the same resource group a
 
    [![trace data](./media/how-to-enable-app-insights/model-data-trace.png)](././media/how-to-enable-app-insights/model-data-trace.png#lightbox)
 
-For more information on how to use Azure Application Insights, see [What is Application Insights?](../azure-monitor/app/app-insights-overview.md).
+For more information on how to use Azure Application Insights, see [What is Application Insights?](../../azure-monitor/app/app-insights-overview.md).
 
 ## Web service metadata and response data
 
@@ -212,9 +215,9 @@ To log web service request information, add `print` statements to your score.py
 ## Export data for retention and processing
 
 >[!Important]
-> Azure Application Insights only supports exports to blob storage. For more information on the limits of this implementation, see [Export telemetry from App Insights](../azure-monitor/app/export-telemetry.md#continuous-export-advanced-storage-configuration).
+> Azure Application Insights only supports exports to blob storage. For more information on the limits of this implementation, see [Export telemetry from App Insights](../../azure-monitor/app/export-telemetry.md#continuous-export-advanced-storage-configuration).
 
-Use Application Insights' [continuous export](../azure-monitor/app/export-telemetry.md) to export data to a blob storage account where you can define retention settings. Application Insights exports the data in JSON format. 
+Use Application Insights' [continuous export](../../azure-monitor/app/export-telemetry.md) to export data to a blob storage account where you can define retention settings. Application Insights exports the data in JSON format. 
 
 :::image type="content" source="media/how-to-enable-app-insights/continuous-export-setup.png" alt-text="Continuous export":::
 
@@ -223,8 +226,8 @@ Use Application Insights' [continuous export](../azure-monitor/app/export-teleme
 In this article, you learned how to enable logging and view logs for web service endpoints. Try these articles for next steps:
 
 
-* [How to deploy a model to an AKS cluster](./v1/how-to-deploy-azure-kubernetes-service.md)
+* [How to deploy a model to an AKS cluster](how-to-deploy-azure-kubernetes-service.md)
 
-* [How to deploy a model to Azure Container Instances](./v1/how-to-deploy-azure-container-instance.md)
+* [How to deploy a model to Azure Container Instances](how-to-deploy-azure-container-instance.md)
 
-* [MLOps: Manage, deploy, and monitor models with Azure Machine Learning](./concept-model-management-and-deployment.md) to learn more about leveraging data collected from models in production. Such data can help to continually improve your machine learning process.
+* [MLOps: Manage, deploy, and monitor models with Azure Machine Learning](concept-model-management-and-deployment.md) to learn more about leveraging data collected from models in production. Such data can help to continually improve your machine learning process.