fix broken links from OPS report

craigcaseyMSFT · craigcaseyMSFT · commit d3560e39bf77 · 2020-04-18T11:04:53.000-07:00
diff --git a/articles/azure-monitor/platform/rest-api-walkthrough.md b/articles/azure-monitor/platform/rest-api-walkthrough.md
@@ -307,7 +307,7 @@ Use the metric’s name ‘value’ (not the ‘localizedValue’) for any filte
 
 **Method**: GET
 
-**Request URI**: https://management.azure.com/subscriptions/*{subscription-id}*/resourceGroups/*{resource-group-name}*/providers/*{resource-provider-namespace}*/*{resource-type}*/*{resource-name}*/providers/microsoft.insights/metrics?metricnames=*{metric}*&timespan=*{starttime/endtime}*&$filter=*{filter}*&interval=*{timeGrain}*&aggregation=*{aggreation}*&api-version=*{apiVersion}*
+**Request URI**: https:\//management.azure.com/subscriptions/*{subscription-id}*/resourceGroups/*{resource-group-name}*/providers/*{resource-provider-namespace}*/*{resource-type}*/*{resource-name}*/providers/microsoft.insights/metrics?metricnames=*{metric}*&timespan=*{starttime/endtime}*&$filter=*{filter}*&interval=*{timeGrain}*&aggregation=*{aggreation}*&api-version=*{apiVersion}*
 
 For example, to retrieve the top 3 APIs, in descending value, by the number of 'Transactions' during a 5 min range, where the GeotType was 'Primary', the request would be as follows:
 
@@ -459,7 +459,7 @@ Once the available metric definitions are known, it is then possible to retrieve
 
 **Method**: GET
 
-**Request URI**: `https://management.azure.com/subscriptions/\*{subscription-id}*/resourceGroups/*{resource-group-name}*/providers/*{resource-provider-namespace}*/*{resource-type}*/*{resource-name}*/providers/microsoft.insights/metrics?$filter=*{filter}*&api-version=*{apiVersion}*`
+**Request URI**: `https:\//management.azure.com/subscriptions/\*{subscription-id}*/resourceGroups/*{resource-group-name}*/providers/*{resource-provider-namespace}*/*{resource-type}*/*{resource-name}*/providers/microsoft.insights/metrics?$filter=*{filter}*&api-version=*{apiVersion}*`
 
 For example, to retrieve the RunsSucceeded metric data points for the given time range and for a time grain of 1 hour, the request would be as follows:
 
diff --git a/articles/cdn/create-profile-resource-manager-template.md b/articles/cdn/create-profile-resource-manager-template.md
@@ -207,7 +207,7 @@ az cdn endpoint list --profile-name standard-microsoft --resource-group cdn -o t
 
 ![View CDN Endpoint](./media/create-profile-resource-manager-template/cdn-view-endpoint.png)
 
-Use the HostName to view the content. For example, access https://cdndemo-azurewebsites-net.azureedge.net using your browser.
+Use the HostName to view the content. For example, access https:\//cdndemo-azurewebsites-net.azureedge.net using your browser.
 
 ## Clean up
 
diff --git a/articles/container-instances/container-instances-managed-identity.md b/articles/container-instances/container-instances-managed-identity.md
@@ -184,7 +184,7 @@ token=$(curl 'http://169.254.169.254/metadata/identity/oauth2/token?api-version=
 
 ```
 
-Now use the access token to authenticate to key vault and read a secret. Be sure to substitute the name of your key vault in the URL (*https://mykeyvault.vault.azure.net/...*):
+Now use the access token to authenticate to key vault and read a secret. Be sure to substitute the name of your key vault in the URL (*https:\//mykeyvault.vault.azure.net/...*):
 
 ```bash
 curl https://mykeyvault.vault.azure.net/secrets/SampleSecret/?api-version=2016-10-01 -H "Authorization: Bearer $token"
diff --git a/articles/container-service/kubernetes/container-service-kubernetes-coscale.md b/articles/container-service/kubernetes/container-service-kubernetes-coscale.md
@@ -58,7 +58,7 @@ az acs kubernetes install-cli
 [DaemonSets](https://kubernetes.io/docs/concepts/workloads/controllers/daemonset/) are used by Kubernetes to run a single instance of a container on each host in the cluster.
 They're perfect for running monitoring agents such as the CoScale agent.
 
-After you log in to CoScale, go to the [agent page](https://app.coscale.com/)
+After you log in to CoScale, go to the [agent page](https://developer.newrelic.com/)
 to install CoScale agents on your cluster using a DaemonSet. The CoScale UI provides guided configuration steps to create an agent and start monitoring your complete Kubernetes cluster.
 
 ![CoScale agent configuration](./media/container-service-kubernetes-coscale/installation.png)
@@ -68,7 +68,7 @@ To start the agent on the cluster, run the supplied command:
 ![Start the CoScale agent](./media/container-service-kubernetes-coscale/agent_script.png)
 
 That's it! Once the agents are up and running, you should see data in the console in a few minutes. Visit
-the [agent page](https://app.coscale.com/) to see a summary of your cluster, perform additional configuration steps, and see dashboards such as the **Kubernetes cluster overview**.
+the [agent page](https://developer.newrelic.com/) to see a summary of your cluster, perform additional configuration steps, and see dashboards such as the **Kubernetes cluster overview**.
 
 ![Kubernetes cluster overview](./media/container-service-kubernetes-coscale/dashboard_clusteroverview.png)
 
diff --git a/articles/databox-online/data-box-gateway-deploy-connect-setup-activate.md b/articles/databox-online/data-box-gateway-deploy-connect-setup-activate.md
@@ -37,7 +37,7 @@ Before you configure and set up your Data Box Gateway, make sure that:
 
 1. Open a browser window and access the local web UI of the device at:
    
-   [https://ip-address-of-network-interface](https://ip-address-of-network-interface)
+   https:\//ip-address-of-network-interface
    
    Use the connection URL noted in the previous tutorial. You see an error or a warning indicating that there is a problem with the website's security certificate.
 
diff --git a/articles/dev-spaces/how-to/setup-cicd.md b/articles/dev-spaces/how-to/setup-cicd.md
@@ -127,7 +127,7 @@ An automated release process will now begin, deploying the *mywebapi* and *webfr
 The release is done when all tasks are complete.
 
 > [!TIP]
-> If your release fails with an error message like *UPGRADE FAILED: timed out waiting for the condition*, try inspecting the pods in your cluster [using the Kubernetes dashboard](../../aks/kubernetes-dashboard.md). If you see the pods are failing to start with error messages like *Failed to pull image "azdsexample.azurecr.io/mywebapi:122": rpc error: code = Unknown desc = Error response from daemon: Get https://azdsexample.azurecr.io/v2/mywebapi/manifests/122: unauthorized: authentication required*, it may be because your cluster has not been authorized to pull from your Azure Container Registry. Make sure you have completed the [Authorize your AKS cluster to pull from your Azure Container Registry](../../aks/cluster-container-registry-integration.md) prerequisite.
+> If your release fails with an error message like *UPGRADE FAILED: timed out waiting for the condition*, try inspecting the pods in your cluster [using the Kubernetes dashboard](../../aks/kubernetes-dashboard.md). If you see the pods are failing to start with error messages like *Failed to pull image "azdsexample.azurecr.io/mywebapi:122": rpc error: code = Unknown desc = Error response from daemon: Get https:\//azdsexample.azurecr.io/v2/mywebapi/manifests/122: unauthorized: authentication required*, it may be because your cluster has not been authorized to pull from your Azure Container Registry. Make sure you have completed the [Authorize your AKS cluster to pull from your Azure Container Registry](../../aks/cluster-container-registry-integration.md) prerequisite.
 
 You now have a fully automated CI/CD pipeline for your GitHub fork of the Dev Spaces sample apps. Each time you commit and push code, the build pipeline will build and push the *mywebapi* and *webfrontend* images to your custom ACR instance. Then the release pipeline will deploy the Helm chart for each app into the _dev_ space on your Dev Spaces-enabled cluster.
 
diff --git a/articles/event-hubs/security-baseline.md b/articles/event-hubs/security-baseline.md
@@ -332,7 +332,7 @@ How to use Azure Policy: https://docs.microsoft.com/azure/governance/policy/tuto
 
 ### 3.4: Use single sign-on (SSO) with Azure Active Directory
 
-**Guidance**: Microsoft Azure provides integrated access control management for resources and applications based on Azure Active Directory (AD). A key advantage of using Azure AD with Azure Event Hubs is that you don't need to store your credentials in the code anymore. Instead, you can request an OAuth 2.0 access token from the Microsoft Identity platform. The resource name to request a token is https://eventhubs.azure.net/. Azure AD authenticates the security principal (a user, group, or service principal) running the application. If the authentication succeeds, Azure AD returns an access token to the application, and the application can then use the access token to authorize request to Azure Event Hubs resources.
+**Guidance**: Microsoft Azure provides integrated access control management for resources and applications based on Azure Active Directory (AD). A key advantage of using Azure AD with Azure Event Hubs is that you don't need to store your credentials in the code anymore. Instead, you can request an OAuth 2.0 access token from the Microsoft Identity platform. The resource name to request a token is https:\//eventhubs.azure.net/. Azure AD authenticates the security principal (a user, group, or service principal) running the application. If the authentication succeeds, Azure AD returns an access token to the application, and the application can then use the access token to authorize request to Azure Event Hubs resources.
 
 How to authenticate an application with Azure AD to access Event Hubs resources: https://docs.microsoft.com/azure/event-hubs/authenticate-application
 
diff --git a/articles/expressroute/expressroute-locations.md b/articles/expressroute/expressroute-locations.md
@@ -275,7 +275,7 @@ If you are remote and don't have fiber connectivity or you want to explore other
 | **[RETN](https://retn.net/services/cloud-connect/)** | Equinix | Amsterdam |
 | **Rogers** | Cologix, Equinix | Montreal, Toronto |
 | **[Spectrum Enterprise](https://enterprise.spectrum.com/services/cloud/cloud-connect.html)** | Equinix | Chicago, Dallas, Los Angeles, New York, Silicon Valley | 
-| **[Tamares Telecom](https://www.tamarestelecom.com/our-services/#Connectivity)** | Equinix | London | 
+| **[Tamares Telecom](http://www.tamarestelecom.com/our-services/#Connectivity)** | Equinix | London | 
 | **[TDC Erhverv](https://tdc.dk/Produkter/cloudaccessplus)** | Equinix | Amsterdam | 
 | **[Telecom Italia Sparkle](https://www.tisparkle.com/our-platform/corporate-platform/sparkle-cloud-connect#catalogue)**| Equinix | Amsterdam |
 | **[Telekom Deutschland GmbH](https://cloud.telekom.de/de/infrastruktur/managed-it-services/managed-hybrid-infrastructure-mit-microsoft-azure)** | Interxion | Amsterdam, Frankfurt |
diff --git a/articles/iot-accelerators/howto-opc-vault-deploy.md b/articles/iot-accelerators/howto-opc-vault-deploy.md
@@ -103,7 +103,7 @@ Use a short and simple resource group name. The name is also used to name resour
 
 ### Website name already in use
 
-It's possible that the name of the website is already in use. You need to use a different resource group name. The hostnames in use by the deployment script are: https://resourcegroupname.azurewebsites.net and https://resourgroupname-service.azurewebsites.net.
+It's possible that the name of the website is already in use. You need to use a different resource group name. The hostnames in use by the deployment script are: https:\//resourcegroupname.azurewebsites.net and https:\//resourgroupname-service.azurewebsites.net.
 Other names of services are built by the combination of short name hashes, and are unlikely to conflict with other services.
 
 ### Azure AD registration 
diff --git a/articles/key-vault/general/service-to-service-authentication.md b/articles/key-vault/general/service-to-service-authentication.md
@@ -85,7 +85,7 @@ To use Azure CLI:
 
 1. Sign in to the Azure portal: *az login* to sign in to Azure.
 
-1. Verify access by entering *az account get-access-token --resource https://vault.azure.net*. If you receive an error, check that the right version of Azure CLI is correctly installed.
+1. Verify access by entering *az account get-access-token --resource https:\//vault.azure.net*. If you receive an error, check that the right version of Azure CLI is correctly installed.
 
    If Azure CLI isn't installed to the default directory, you may receive an error reporting that `AzureServiceTokenProvider` can't find the path for Azure CLI. Use the **AzureCLIPath** environment variable to define the Azure CLI installation folder. `AzureServiceTokenProvider` adds the directory specified in the **AzureCLIPath** environment variable to the **Path** environment variable when necessary.
 
diff --git a/articles/machine-learning/azure-machine-learning-release-notes.md b/articles/machine-learning/azure-machine-learning-release-notes.md
@@ -820,7 +820,7 @@ The Experiment tab in the [new workspace portal](https://ml.azure.com) has been
 ### Azure Machine Learning SDK for Python v1.0.62
 
 + **New features**
-  + Introduced the `timeseries`  trait on TabularDataset. This trait enables easy timestamp filtering on data a TabularDataset, such as taking all data between a range of time or the most recent data. To learn about this the `timeseries`  trait on TabularDataset, please visit https://aka.ms/azureml-data for documentation or https://aka.ms/azureml-tsd-notebook for an example notebook.
+  + Introduced the `timeseries`  trait on TabularDataset. This trait enables easy timestamp filtering on data a TabularDataset, such as taking all data between a range of time or the most recent data. To learn about this the `timeseries`  trait on TabularDataset, please visit https://aka.ms/azureml-data for documentation or https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/work-with-data/datasets-tutorial/timeseries-datasets/tabular-timeseries-dataset-filtering.ipynb for an example notebook.
   + Enabled training with TabularDataset and FileDataset. Please visit https://aka.ms/dataset-tutorial for an example notebook.
 
   + **azureml-train-core**
@@ -1334,7 +1334,7 @@ We reverted a change that improved performance, as it was causing issues for som
 + **Preview features**
     + Integration with [MLflow](https://mlflow.org) 1.0.0 tracking through azureml-mlflow package ([example notebooks](https://aka.ms/azureml-mlflow-examples)).
     + Submit Jupyter notebook as a run. [API Reference Documentation](https://docs.microsoft.com/python/api/azureml-contrib-notebook/azureml.contrib.notebook?view=azure-ml-py)
-    + Public Preview of [Data Drift Detector](https://docs.microsoft.com/python/api/azureml-datadrift/azureml.datadrift.datadriftdetector(class)) through azureml-contrib-datadrift package ([example notebooks](https://aka.ms/azureml-datadrift-example)). Data Drift is one of the top reasons where model accuracy degrades over time. It happens when data served to model in production is different from the data that the model was trained on. AML Data Drift detector helps customer to monitor data drift and sends alert whenever drift is detected.
+    + Public Preview of [Data Drift Detector](https://docs.microsoft.com/python/api/azureml-datadrift/azureml.datadrift.datadriftdetector(class)) through azureml-contrib-datadrift package ([example notebooks](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/monitor-models/data-drift)). Data Drift is one of the top reasons where model accuracy degrades over time. It happens when data served to model in production is different from the data that the model was trained on. AML Data Drift detector helps customer to monitor data drift and sends alert whenever drift is detected.
 
 + **Breaking changes**
 
diff --git a/articles/machine-learning/data-science-virtual-machine/vm-do-ten-things.md b/articles/machine-learning/data-science-virtual-machine/vm-do-ten-things.md
@@ -815,7 +815,7 @@ Use the following prerequisite steps to access Azure Cosmos DB from the DSVM:
 1. The Azure Cosmos DB Python SDK is already installed on the DSVM. To update it, run ```pip install pydocumentdb --upgrade``` from a command prompt.
 2. Create an Azure Cosmos DB account and database from the [Azure portal](https://portal.azure.com).
 3. Download the Azure Cosmos DB Data Migration Tool from the [Microsoft Download Center](https://www.microsoft.com/download/details.aspx?id=53595) and extract to a directory of your choice.
-4. Import JSON data (volcano data) stored in a [public blob](https://cahandson.blob.core.windows.net/samples/volcano.json) into Azure Cosmos DB with the following command parameters to the migration tool. (Use dtui.exe from the directory where you installed the Azure Cosmos DB Data Migration Tool.) Enter the source and target location with these parameters:
+4. Import JSON data (volcano data) stored in a [public blob](https://dotnet.microsoft.com/) into Azure Cosmos DB with the following command parameters to the migration tool. (Use dtui.exe from the directory where you installed the Azure Cosmos DB Data Migration Tool.) Enter the source and target location with these parameters:
    
     `/s:JsonFile /s.Files:https://cahandson.blob.core.windows.net/samples/volcano.json /t:DocumentDBBulk /t.ConnectionString:AccountEndpoint=https://[DocDBAccountName].documents.azure.com:443/;AccountKey=[[KEY];Database=volcano /t.Collection:volcano1`
 
diff --git a/articles/machine-learning/how-to-create-register-datasets.md b/articles/machine-learning/how-to-create-register-datasets.md
@@ -165,7 +165,7 @@ sql_ds = Dataset.Tabular.from_sql_query((sql_datastore, 'SELECT * FROM my_table'
 
 In TabularDatasets, you can specify a time stamp from a column in the data or from wherever the path pattern data is stored to enable a time series trait. This specification allows for easy and efficient filtering by time.
 
-Use the [`with_timestamp_columns()`](https://docs.microsoft.com/python/api/azureml-core/azureml.data.tabulardataset?view=azure-ml-py#with-timestamp-columns-timestamp-none--partition-timestamp-none--validate-false----kwargs-) method on the`TabularDataset` class to specify your time stamp column and to enable filtering by time. For more information, see [Tabular time series-related API demo with NOAA weather data](https://aka.ms/azureml-tsd-notebook).
+Use the [`with_timestamp_columns()`](https://docs.microsoft.com/python/api/azureml-core/azureml.data.tabulardataset?view=azure-ml-py#with-timestamp-columns-timestamp-none--partition-timestamp-none--validate-false----kwargs-) method on the`TabularDataset` class to specify your time stamp column and to enable filtering by time. For more information, see [Tabular time series-related API demo with NOAA weather data](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/work-with-data/datasets-tutorial/timeseries-datasets/tabular-timeseries-dataset-filtering.ipynb).
 
 ```Python
 # create a TabularDataset with time series trait
diff --git a/articles/machine-learning/how-to-monitor-datasets.md b/articles/machine-learning/how-to-monitor-datasets.md
@@ -101,7 +101,7 @@ dset = dset.with_timestamp_columns('date')
 dset = dset.register(ws, 'target')
 ```
 
-For a full example of using the `timeseries` trait of datasets, see the [example notebook](https://aka.ms/azureml-tsd-notebook) or the [datasets SDK documentation](https://docs.microsoft.com/python/api/azureml-core/azureml.data.tabulardataset?view=azure-ml-py#with-timestamp-columns-timestamp-none--partition-timestamp-none--validate-false----kwargs-).
+For a full example of using the `timeseries` trait of datasets, see the [example notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/work-with-data/datasets-tutorial/timeseries-datasets/tabular-timeseries-dataset-filtering.ipynb) or the [datasets SDK documentation](https://docs.microsoft.com/python/api/azureml-core/azureml.data.tabulardataset?view=azure-ml-py#with-timestamp-columns-timestamp-none--partition-timestamp-none--validate-false----kwargs-).
 
 #### Azure Machine Learning studio
 [!INCLUDE [applies-to-skus](../../includes/aml-applies-to-enterprise-sku-inline.md)]
diff --git a/articles/machine-learning/how-to-train-ml-models.md b/articles/machine-learning/how-to-train-ml-models.md
@@ -77,7 +77,7 @@ print(run.get_portal_url())
 >
 > To create artifacts during training (such as model files, checkpoints, data files, or plotted images) write these to the `./outputs` folder.
 >
-> Similarly, you can write any logs from your training run to the `./logs` folder. To utilize Azure Machine Learning's [TensorBoard integration](https://aka.ms/aml-notebook-tb) make sure you write your TensorBoard logs to this folder. While your run is in progress, you will be able to launch TensorBoard and stream these logs.  Later, you will also be able to restore the logs from any of your previous runs.
+> Similarly, you can write any logs from your training run to the `./logs` folder. To utilize Azure Machine Learning's [TensorBoard integration](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/training-with-deep-learning/export-run-history-to-tensorboard/export-run-history-to-tensorboard.ipynb) make sure you write your TensorBoard logs to this folder. While your run is in progress, you will be able to launch TensorBoard and stream these logs.  Later, you will also be able to restore the logs from any of your previous runs.
 >
 > For example, to download a file written to the *outputs* folder to your local machine after your remote training run: 
 > `run.download_file(name='outputs/my_output_file', output_file_path='my_destination_path')`
