fix build warnings

msakande · msakande · commit 50cb788e7882 · 2024-08-09T13:17:59.000-05:00
diff --git a/articles/machine-learning/concept-endpoints-online.md b/articles/machine-learning/concept-endpoints-online.md
@@ -122,7 +122,7 @@ The following table describes the key attributes of a deployment:
 |-----------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
 | Name           | The name of the deployment.                                                                                                                                                                                                                                                                                                                                                                    |
 | Endpoint name  | The name of the endpoint to create the deployment under.                                                                                                                                                                                                                                                                                                                                       |
-| Model<sup>1</sup>   | The model to use for the deployment. This value can be either a reference to an existing versioned model in the workspace or an inline model specification. For more information on how to track and specify the path to your model, see [Model specification in an online deployment configuration](concept-online-deployment-model-specification.md).                                                                                                                                                                                                                                    |
+| Model<sup>1</sup>   | The model to use for the deployment. This value can be either a reference to an existing versioned model in the workspace or an inline model specification. For more information on how to track and specify the path to your model, see [Specify model to deploy for use in online endpoint](concept-online-deployment-model-specification.md).                                                                                                                                                                                                                                    |
 | Code path      | The path to the directory on the local development environment that contains all the Python source code for scoring the model. You can use nested directories and packages.                                                                                                                                                                                                                    |
 | Scoring script | The relative path to the scoring file in the source code directory. This Python code must have an `init()` function and a `run()` function. The `init()` function will be called after the model is created or updated (you can use it to cache the model in memory, for example). The `run()` function is called at every invocation of the endpoint to do the actual scoring and prediction. |
 | Environment<sup>1</sup>    | The environment to host the model and code. This value can be either a reference to an existing versioned environment in the workspace or an inline environment specification. __Note:__ Microsoft regularly patches the base images for known security vulnerabilities. You'll need to redeploy your endpoint to use the patched image. If you provide your own image, you're responsible for updating it. For more information, see [Image patching](concept-environments.md#image-patching).                                                                                                                                                                                                            |
diff --git a/articles/machine-learning/concept-online-deployment-model-specification.md b/articles/machine-learning/concept-online-deployment-model-specification.md
@@ -2,8 +2,8 @@
 title: Model specification for online deployments
 titleSuffix: Azure Machine Learning
 description: Specify the model to use in an Azure Machine Learning online endpoint's deployment.
-services: machine-learning
-ms.service: machine-learning
+manager: scottpolly
+ms.service: azure-machine-learning
 ms.subservice: inferencing
 author: dem108
 ms.author: sehan
diff --git a/articles/machine-learning/reference-managed-online-endpoints-vm-sku-list.md b/articles/machine-learning/reference-managed-online-endpoints-vm-sku-list.md
@@ -30,7 +30,7 @@ The following table shows the virtual machine (VM) stock keeping units (SKUs) th
 | X-Large       | Standard_D32a_v4 </br> Standard_D32as_v4 </br> Standard_D48a_v4 </br> Standard_D48as_v4 </br> Standard_D64a_v4 </br> Standard_D64as_v4 </br> Standard_D96a_v4 </br> Standard_D96as_v4 | Standard_F32s_v2 <br/> Standard_F48s_v2 <br/> Standard_F64s_v2 <br/> Standard_F72s_v2 <br/> Standard_FX24mds <br/> Standard_FX36mds <br/> Standard_FX48mds | Standard_E32s_v3 <br/> Standard_E48s_v3 <br/> Standard_E64s_v3 | Standard_NC48ads_A100_v4 </br> Standard_NC96ads_A100_v4 </br> Standard_ND96asr_v4 </br> Standard_ND96amsr_A100_v4 </br> Standard_ND40rs_v2 |
 
 > [!CAUTION]
-> `Standard_DS1_v2` and `Standard_F2s_v2` may be too small for bigger models and may lead to container termination due to insufficient memory, not enough space on the disk, or probe failure as it takes too long to initiate the container. If you face [OutOfQuota errors](how-to-troubleshoot-online-endpoints.md?tabs=cli#error-outofquota) or [ReourceNotReady errors](how-to-troubleshoot-online-endpoints.md?tabs=cli#error-resourcenotready), try bigger VM SKUs. If you want to reduce the cost of deploying multiple models with managed online endpoint, see [Deployment configuration with several local models](concept-online-deployment-model-specification.md#deployment-configuration-with-several-local-models).
+> `Standard_DS1_v2` and `Standard_F2s_v2` may be too small for bigger models and may lead to container termination due to insufficient memory, not enough space on the disk, or probe failure as it takes too long to initiate the container. If you face [OutOfQuota errors](how-to-troubleshoot-online-endpoints.md?tabs=cli#error-outofquota) or [ReourceNotReady errors](how-to-troubleshoot-online-endpoints.md?tabs=cli#error-resourcenotready), try bigger VM SKUs. If you want to reduce the cost of deploying multiple models with managed online endpoint, see [Deployment for several local models](concept-online-deployment-model-specification.md#deployment-for-several-local-models).
 
 > [!NOTE]
 > We recommend having more than 3 instances for deployments in production scenarios. In addition, Azure Machine Learning reserves 20% of your compute resources for performing upgrades on some VM SKUs as described in [Virtual machine quota allocation for deployment](how-to-manage-quotas.md#virtual-machine-quota-allocation-for-deployment). VM SKUs that are exempted from this extra quota reservation are listed below:
diff --git a/articles/machine-learning/toc.yml b/articles/machine-learning/toc.yml
@@ -911,7 +911,7 @@
       items:
         - name: Online endpoints
           href: concept-endpoints-online.md
-        - name: Model specification in online deployment configuration
+        - name: Model specification for online deployment
           href: concept-online-deployment-model-specification.md
         - name: Deploy an ML model with an online endpoint
           href: how-to-deploy-online-endpoints.md
