Bulk fix absolute links

Author: JasonWHowell

articles/industrial-iot/tutorial-publisher-performance-memory-tuning-opc-publisher.md

@@ -60,7 +60,7 @@ To run OPC Publisher in production, network performance requirements (throughput
 
 ## Latency considerations
 
-What is typically seen as latency is the time difference between the `iothub-enqueuedtime` of the [device to cloud message](https://learn.microsoft.com/azure/iot-hub/iot-hub-devguide-messages-construct) and the `SourceTimestamp` field of an OPC UA telemetry event. There are multiple factors, which contribute to the latency:
+What is typically seen as latency is the time difference between the `iothub-enqueuedtime` of the [device to cloud message](/azure/iot-hub/iot-hub-devguide-messages-construct) and the `SourceTimestamp` field of an OPC UA telemetry event. There are multiple factors, which contribute to the latency:
 * The `SourceTimestamp` of the OPC UA telemetry event is a value [defined by the OPC UA Specification](https://reference.opcfoundation.org/Core/Part4/v104/7.7.3/) as to be as close to the source of the value. The origin of `SourceTimestamp` is highly dependent on the setup between sensor and OPC UA server. Independent from the setup, it's important to ensure that the time source is synchronized precisely otherwise the latency calculation will be not correct.
 * It's important that the systems and interconnection between the sensor and the IoT Edge host system where OPC Publisher runs is stable and doesn't introduce latency.
 * The configuration of the OPC UA nodes to publish and the effect of OPC Publisher command line options on latency will be discussed below.
@@ -116,7 +116,7 @@ OPC Publisher maintains an internal queue for all data change notifications. The
 
 If the queue keeps growing even though the `--si` and `--ms` have been adjusted and the queue capacity will be reached, messages will be discarded. The reason can be that the time it takes to send a message to IoT Hub doesn't provide the required throughput. Since this time is made up of multiple parts to understand if there's a bottleneck several areas should be validated:
 - Validation that the IoT Edge host network connection to the IoT Hub is stable and has low latency.
-- Validation that the modules running in IoT Edge (OPC Publisher, edgeHub, and others) don't hit any limits for CPU and memory consumption. Additionally use of the [IoT Edge metrics collector](https://learn.microsoft.com/azure/iot-edge/how-to-collect-and-transport-metrics?view=iotedge-1.4&tabs=iothub) can give insights on resource usage of the system.
+- Validation that the modules running in IoT Edge (OPC Publisher, edgeHub, and others) don't hit any limits for CPU and memory consumption. Additionally use of the [IoT Edge metrics collector](/azure/iot-edge/how-to-collect-and-transport-metrics?tabs=iothub) can give insights on resource usage of the system.
 - Validation that the time to ingest a message from an IoT Edge module not using any OPC UA data does meet expectations even with active workload.
 
 If the capacity of the internal message queue is used and there are still incoming notifications from the OPC UA server, data change notifications will be discarded. The diagnostics output will show the number of discarded messages.

articles/iot-hub-device-update/delta-updates.md

@@ -74,7 +74,7 @@ The following table provides a list of the content needed, where to retrieve the
 
 | Binary Name | Where to acquire | How to install |
 |--|--|--|
-| DiffGen | [Azure/iot-hub-device-update-delta](https://github.com/Azure/iot-hub-device-update-delta) GitHub repo | From the root folder, select the _Microsoft.Azure.DeviceUpdate.Diffs.[version].nupkg_ file. [Learn more about NuGet packages](https://learn.microsoft.com/nuget/).|
+| DiffGen | [Azure/iot-hub-device-update-delta](https://github.com/Azure/iot-hub-device-update-delta) GitHub repo | From the root folder, select the _Microsoft.Azure.DeviceUpdate.Diffs.[version].nupkg_ file. [Learn more about NuGet packages](/nuget/).|
 | .NET (Runtime) | Via Terminal / Package Managers | [Instructions for Linux](/dotnet/core/install/linux). Only the Runtime is required. |
 
 ### Dependencies

articles/machine-learning/concept-designer.md

@@ -2,7 +2,6 @@
 title: What is the Azure Machine Learning designer(v2)?
 titleSuffix: Azure Machine Learning
 description: Learn what the Azure Machine Learning designer is and what tasks you can use it for. The drag-and-drop UI enables customer to build machine learning pipeline. 
-services: machine-learning
 ms.service: machine-learning
 ms.subservice: core
 ms.topic: conceptual
@@ -38,7 +37,7 @@ As shown in below GIF, you can build a pipeline visually by dragging and droppin
 
 The building blocks of pipeline are called assets in Azure Machine Learning, which includes:
  - [Data](./concept-data.md)
- - [Model](https://learn.microsoft.com/azure/machine-learning/how-to-manage-models?view=azureml-api-2&tabs=cli%2Cuse-local)
+ - [Model](how-to-manage-models.md?view=azureml-api-2&preserve-view=true&tabs=cli)
  - [Component](./concept-component.md)
 
 Designer has an asset library on the left side, where you can access all the assets you need to create your pipeline. It shows both the assets you created in your workspace, and the assets shared in [registry](./how-to-share-models-pipelines-across-workspaces-with-registries.md) that you have permission to access.

articles/machine-learning/how-to-create-component-pipeline-python.md

@@ -2,7 +2,6 @@
 title: 'Create and run machine learning pipelines using components with the Azure Machine Learning SDK v2'
 titleSuffix: Azure Machine Learning
 description: Build a machine learning pipeline for image classification. Focus on machine learning instead of infrastructure and automation.
-services: machine-learning
 ms.service: machine-learning
 ms.subservice: mlops
 ms.topic: how-to
@@ -256,7 +255,7 @@ Reference for more available credentials if it doesn't work for you: [configure
 
 #### Get a handle to a workspace with compute
 
-Create a `MLClient` object to manage Azure Machine Learning services. If you use [serverless compute (preview)](https://learn.microsoft.com/azure/machine-learning/how-to-use-serverless-compute?view=azureml-api-2&tabs=python) then there is no need to create these computes.
+Create a `MLClient` object to manage Azure Machine Learning services. If you use [serverless compute (preview)](how-to-use-serverless-compute.md?view=azureml-api-2&preserve-view=true&tabs=python) then there is no need to create these computes.
 
 [!notebook-python[] (~/azureml-examples-main/sdk/python/jobs/pipelines/2e_image_classification_keras_minist_convnet/image_classification_keras_minist_convnet.ipynb?name=workspace)]
 

articles/machine-learning/how-to-setup-mlops-azureml.md

@@ -338,7 +338,7 @@ This scenario includes prebuilt workflows for two approaches to deploying a trai
 * [Install and set up Python SDK v2](https://aka.ms/sdk-v2-install)
 * [Install and set up Python CLI v2](how-to-configure-cli.md)
 * [Azure MLOps (v2) solution accelerator](https://github.com/Azure/mlops-v2) on GitHub
-* Training course on [MLOps with Machine Learning](https://learn.microsoft.com/training/paths/introduction-machine-learn-operations/)
+* Training course on [MLOps with Machine Learning](/training/paths/introduction-machine-learn-operations/)
 * Learn more about [Azure Pipelines with Azure Machine Learning](how-to-devops-machine-learning.md)
 * Learn more about [GitHub Actions with Azure Machine Learning](how-to-github-actions-machine-learning.md)
 * Deploy MLOps on Azure in Less Than an Hour - [Community MLOps V2 Accelerator video](https://www.youtube.com/watch?v=5yPDkWCMmtk)

articles/machine-learning/how-to-setup-mlops-github-azure-ml.md

@@ -301,7 +301,7 @@ The sample training and deployment Machine Learning pipelines and GitHub workflo
 * [Install and set up Python SDK v2](https://aka.ms/sdk-v2-install)
 * [Install and set up Python CLI v2](how-to-configure-cli.md)
 * [Azure MLOps (v2) solution accelerator](https://github.com/Azure/mlops-v2) on GitHub
-* Training course on [MLOps with Machine Learning](https://learn.microsoft.com/training/paths/introduction-machine-learn-operations/)
+* Training course on [MLOps with Machine Learning](/training/paths/introduction-machine-learn-operations/)
 * Learn more about [Azure Pipelines with Machine Learning](how-to-devops-machine-learning.md)
 * Learn more about [GitHub Actions with Machine Learning](how-to-github-actions-machine-learning.md)
 * Deploy MLOps on Azure in Less Than an Hour - [Community MLOps V2 Accelerator video](https://www.youtube.com/watch?v=5yPDkWCMmtk)

articles/machine-learning/known-issues/azure-machine-learning-known-issues.md

@@ -33,4 +33,4 @@ Select the **Title** to view more information about that specific known issue.
 
 
 - [See Azure service level outages](https://azure.status.microsoft/status)
-- [Get your questions answered by the Azure Machine Learning community](https://learn.microsoft.com/answers/tags/75/azure-machine-learning)
+- [Get your questions answered by the Azure Machine Learning community](/answers/tags/75/azure-machine-learning)

articles/machine-learning/known-issues/compute-a10-sku-not-supported.md

@@ -25,7 +25,7 @@ While trying to create a compute instance with A10 SKU, you'll encounter a provi
 
 ## Solutions and workarounds
 
-A10 AKUs aren't supported for compute instances. Consult this list of supported SKUs: [Supported VM series and sizes](https://learn.microsoft.com/azure/machine-learning/concept-compute-target?view=azureml-api-2#supported-vm-series-and-sizes&preserve-view=true)
+A10 AKUs aren't supported for compute instances. Consult this list of supported SKUs: [Supported VM series and sizes](../concept-compute-target.md?view=azureml-api-2&preserve-view=true#supported-vm-series-and-sizes)
 
 ## Next steps
 

articles/migrate/concepts-azure-sql-assessment-calculation.md

@@ -98,7 +98,7 @@ Azure SQL Database sizing | **Purchase model** | Defaulted to *vCore*.
 Azure SQL Database sizing | **Compute tier** | Defaulted to *Provisioned*.
 High availability and disaster recovery properties | **Disaster recovery region** | Defaulted to the [cross-region replication pair](../reliability/cross-region-replication-azure.md#azure-cross-region-replication-pairings-for-all-geographies) of the Target location. In an unlikely event when the chosen Target location doesn't yet have such a pair, the specified Target location itself is chosen as the default disaster recovery region.
 High availability and disaster recovery properties | **Multi-subnet intent** | Defaulted to Disaster recovery. <br/><br/> Select **Disaster recovery** if you want asynchronous data replication where some replication delays are tolerable. This allows higher durability using geo-redundancy. In the event of failover, data that hasn't yet been replicated may be lost. <br/><br/> Select **High availability** if you desire the data replication to be synchronous and no data loss due to replication delay is allowable. This setting allows assessment to leverage built-in high availability options in Azure SQL Databases and Azure SQL Managed Instances, and availability zones and zone-redundancy in Azure Virtual Machines to provide higher availability. In the event of failover, no data is lost.  
-High availability and disaster recovery properties | **Internet Access** | Defaulted to Available.<br/><br/> Select **Available** if you allow outbound Internet access from Azure VMs. This allows the use of [Cloud Witness](https://learn.microsoft.com/azure/azure-sql/virtual-machines/windows/hadr-cluster-quorum-configure-how-to?view=azuresql&tabs=powershell) which is the recommended approach for Windows Server Failover Clusters in Azure Virtual Machines. <br/><br/> Select **Not available** if the Azure VMs have no outbound Internet access. This requires the use of a Shared Disk as a witness for Windows Server Failover Clusters in Azure Virtual Machines. 
+High availability and disaster recovery properties | **Internet Access** | Defaulted to Available.<br/><br/> Select **Available** if you allow outbound Internet access from Azure VMs. This allows the use of [Cloud Witness](/azure/azure-sql/virtual-machines/windows/hadr-cluster-quorum-configure-how-to?tabs=powershell) which is the recommended approach for Windows Server Failover Clusters in Azure Virtual Machines. <br/><br/> Select **Not available** if the Azure VMs have no outbound Internet access. This requires the use of a Shared Disk as a witness for Windows Server Failover Clusters in Azure Virtual Machines. 
 High availability and disaster recovery properties | **Async commit mode intent** | Defaulted to Disaster recovery. <br/><br/> Select **Disaster recovery** if you're using asynchronous commit availability mode to enable higher durability for the data without affecting performance. In the event of failover, data that hasn't yet been replicated may be lost. <br/><br/> Select **High availability** if you're using asynchronous commit data availability mode to improve availability and scale out read traffic. This setting allows assessment to leverage built-in high availability features in Azure SQL Databases, Azure SQL Managed Instances, and Azure Virtual Machines to provide higher availability and scale out.  
 Security | **Security** | Defaulted to Yes, with Microsoft Defender for Cloud. <br/><br/> Specifies whether you want to assess readiness and cost for security tooling on Azure. If the setting has the default value **Yes, with Microsoft Defender for Cloud**, it will assess security readiness and costs for your Azure SQL MI/DB with Microsoft Defender for Cloud.   
 
@@ -115,7 +115,7 @@ Security | **Security** | Defaulted to Yes, with Microsoft Defender for Cloud. <
 Readiness checks for different migration strategies: 
 
 #### Recommended deployment, Instances to SQL Server on Azure VM, Instances to Azure SQL MI, Database to Azure SQL DB:
-Azure SQL readiness for SQL instances and databases is based on a feature compatibility check with SQL Server on Azure VM, [Azure SQL Database](https://learn.microsoft.com/azure/azure-sql/migration-guides/database/sql-server-to-sql-database-assessment-rules?view=azuresql), and [Azure SQL Managed Instance](https://learn.microsoft.com/azure/azure-sql/migration-guides/managed-instance/sql-server-to-sql-managed-instance-assessment-rules?view=azuresql):
+Azure SQL readiness for SQL instances and databases is based on a feature compatibility check with SQL Server on Azure VM, [Azure SQL Database](/azure/azure-sql/migration-guides/database/sql-server-to-sql-database-assessment-rules), and [Azure SQL Managed Instance](/azure/azure-sql/migration-guides/managed-instance/sql-server-to-sql-managed-instance-assessment-rules):
 1. The Azure SQL assessment considers the SQL Server instance features that are currently used by the source SQL Server workloads (SQL Agent jobs, linked servers, etc.) and the user databases schemas (tables, views, triggers, stored procedures etc.) to identify compatibility issues.
 1. If there are no compatibility issues found, the instance is marked as **Ready** for the target deployment type (SQL Server on Azure VM or Azure SQL Database or Azure SQL Managed Instance)
 1. If there are non-critical compatibility issues, such as deprecated or unsupported features that don't block the migration to a specific target deployment type, the instance is marked as **Ready** (hyperlinked) with **warning** details and recommended remediation guidance. This includes the situation where the source data has an Always On Availability Group configuration and the required replicas exceed those available with the specific target deployment type. 

articles/migrate/concepts-migration-planning.md

@@ -132,7 +132,7 @@ Before finalizing your migration plan, make sure you consider and mitigate other
 - **Network requirements**: Evaluate network bandwidth and latency constraints, which might cause unforeseen delays and disruptions to migration replication speed.
 - **Testing/post-migration tweaks**: Allow a time buffer to conduct performance and user acceptance testing for migrated apps, or to configure/tweak apps post-migration, such as updating database connection strings, configuring web servers, performing cut-overs/cleanup etc.
 - **Permissions**: Review recommended Azure permissions, and server/database access roles and permissions needed for migration.
-- **Training**: Prepare your organization for the digital transformation. A solid training foundation is important for successful organizational change. Check out free [Microsoft Learn training](/training/azure/?ocid=CM_Discovery_Checklist_PDF), including courses on Azure fundamentals, solution architectures, and security. Encourage your team to explore [Azure certifications](https://learn.microsoft.com/certifications). 
+- **Training**: Prepare your organization for the digital transformation. A solid training foundation is important for successful organizational change. Check out free [Microsoft Learn training](/training/azure/?ocid=CM_Discovery_Checklist_PDF), including courses on Azure fundamentals, solution architectures, and security. Encourage your team to explore [Azure certifications](/certifications). 
 - **Implementation support**: Get support for your implementation if you need it. Many organizations opt for outside help to support their cloud migration. To move to Azure quickly and confidently with personalized assistance, consider an [Azure Expert Managed Service Provider](https://www.microsoft.com/solution-providers/search?cacheId=9c2fed4f-f9e2-42fb-8966-4c565f08f11e&ocid=CM_Discovery_Checklist_PDF), or [FastTrack for Azure](https://azure.microsoft.com/programs/azure-fasttrack/?ocid=CM_Discovery_Checklist_PDF).