Ag manufacturing readme update Canary

docs/azure_jumpstart_ag/manufacturing/contoso_motors/troubleshooting/_index.md

@@ -16,14 +16,14 @@
 - Not enough vCPU quota available in your target Azure region - check vCPU quota and ensure you have at least 40 available vCPU.
   - You can use the command *`az vm list-usage --location <your location> --output table`* to check your available vCPU quota.
 
     ![Screenshot showing az vm list-usage](./img/az_vm_list_usage.png)
 
 - Target Azure region does not support all required Azure services - ensure you are running Agora in one of the supported regions listed in the [deployment guide](../deployment/).
 
 - Not enough Microsoft Entra ID quota to create additional service principals. You may receive a message stating "The directory object quota limit for the Principal has been exceeded. Please ask your administrator to increase the quota limit or delete objects to reduce the used quota."
   - If this occurs, you must delete some of your unused service principals and try the deployment again.
 
     ![Screenshot showing not enough Entra quota for new service principals](./img/aad_quota_exceeded.png)
 
 ### Exploring logs from the _Ag-VM-Client_ virtual machine
 
@@ -33,7 +33,7 @@
 | ------- | ----------- |
 | _C:\Ag\Logs\AgLogonScript.log_ | Output from the primary PowerShell script that drives most of the automation tasks. |
 | _C:\Ag\Logs\ArcConnectivity.log_ | Output from the tasks that onboard servers and Kubernetes clusters to Azure Arc. |
 | _C:\Ag\Logs\AzCLI.log_ | Output from Az CLI login. |
 | _C:\Ag\Logs\AzPowerShell.log_ | Output from the installation of PowerShell modules. |
 | _C:\Ag\Logs\Bookmarks.log_ | Output from the configuration of Microsoft Edge bookmarks. |
 | _C:\Ag\Logs\Bootstrap.log_ | Output from the initial bootstrapping script that runs on _Ag-VM-Client_. |
@@ -47,11 +47,11 @@
 
 ### Authorization errors when deploying Azure IoT Operations
 
-If you see authorization errors during the automation, please make sure to review the [prerequisites](../deployment) in the deployment guide.
+If you see authorization errors during the automation, please make sure to review the [prerequisites](../deployment/#prerequisites) in the deployment guide.
 
 ### Error loading dashboards with Azure Data Explorer
 
 If you have access to mulitple Azure environments, you may receive an error when first accessing the dashboards in Azure Data Explorer.
 
 The screenshot below shows this type of error.
 

docs/azure_jumpstart_ag/manufacturing/contoso_motors/welding_defect/_index.md

@@ -18,7 +18,7 @@
 
 ## Overview
 
 Contoso Motors uses AI-enhanced computer vision to improve welding operations on its assembly lines. Welding is one of the four computer vision use cases that Contoso Motors uses, which also include object detection, human pose estimation, and safety helmet detection. While each use case has its own unique characteristics, they all follow the same inferencing architecture pattern and data flow.
 
 Welding is a process of joining two or more metal parts by melting and fusing them together. Welding defects are flaws or irregularities that occur during or after the welding process, which can affect the quality, strength, and appearance of the weld. Welding defects can be caused by various factors, such as improper welding parameters, inadequate preparation, poor welding technique, or environmental conditions. In this scenario, an AI model is used to automatically detect and classify welding defects from a video feed. Welding defect inference can help improve the efficiency, accuracy, and safety of weld inspection and quality control.
 
@@ -57,7 +57,7 @@
 
 ![Welding defect UI](./img/welding_ui.png)
 
-If you're interested in learning more about the AI inference flow, check out the [AI Inference Architecture](./ai_inferencing) page for additional information.
+If you're interested in learning more about the AI inference flow, check out the [AI Inference Architecture](../ai_inferencing/#architecture) page for additional information.
 
 ### Model
 
@@ -81,7 +81,7 @@
 
 Contoso leverages their AI-enhanced computer vision to monitor the welding process and help OT managers detect welding defects through the "Control Center" interface.
 
 - To access the "Control Center" interface, select the Control center [_env_] option from the _Control center_ Bookmarks folder. Each environment will have it's own "Control Center" instance with a different IP. Select one of the sites and click on the factory image to start navigating the different factory control centers.
 
   ![Screenshot showing the Control center Bookmark](./img/control-center-menu.png)
 

docs/azure_jumpstart_ag/manufacturing/contoso_motors/workers_safety/_index.md

@@ -50,7 +50,7 @@ This diagram shows the workers safety inference flow, which consists of five mai
 
     Once the image is processed, is then served to the main application to render it to the user. Final image contains the result of the worker safety inference (**helmet**, **head** and **person**) and the probability of the result.
 
-If you're interested in learning more about the AI inference flow, check out the [AI Inference Architecture](./ai_inferencing) page for additional information.
+If you're interested in learning more about the AI inference flow, check out the [AI Inference Architecture](../ai_inferencing/#architecture) page for additional information.
 
 ### Model