Merge pull request #99407 from liakaz/liakaz/troubleshoot_guide_update

Changes to trouble shooting guide: using Environment

Author: v-albemi

articles/machine-learning/how-to-enable-logging.md

@@ -78,22 +78,6 @@ compute_target = ComputeTarget.attach(
 compute.wait_for_completion(show_output=True)
 ```
 
-## Logging during image creation
-
-Enabling logging during image creation will allow you to see any errors during the build process. Set the `show_output` param on the `wait_for_deployment()` function.
-
-```python
-from azureml.core.webservice import Webservice
-
-service = Webservice.deploy_from_image(deployment_config=your_config,
-                                       image=image,
-                                       name="example-image",
-                                       workspace=ws
-                                       )
-
-service.wait_for_deployment(show_output=True)
-```
-
 ## Logging for deployed models
 
 To retrieve logs from a previously deployed web service, load the service and use the `get_logs()` function. The logs may contain detailed information about any errors that occurred during deployment.

articles/machine-learning/how-to-setup-authentication.md

@@ -165,7 +165,7 @@ ws.get_details()
 
 The service principal created in the steps above can also be used to authenticate to the Azure Machine Learning [REST API](https://docs.microsoft.com/rest/api/azureml/). You use the Azure Active Directory [client credentials grant flow](https://docs.microsoft.com/azure/active-directory/develop/v1-oauth2-client-creds-grant-flow), which allow service-to-service calls for headless authentication in automated workflows. The examples are implemented with the [ADAL library](https://docs.microsoft.com/azure/active-directory/develop/active-directory-authentication-libraries) in both Python and Node.js, but you can also use any open-source library that supports OpenID Connect 1.0. 
 
-> ![NOTE]
+> [!NOTE]
 > MSAL.js is a newer library than ADAL, but you cannot do service-to-service authentication using client credentials with MSAL.js, since it is primarily a client-side library intended 
 > for interactive/UI authentication tied to a specific user. We recommend using ADAL as shown below to build automated workflows with the REST API.
 
@@ -265,15 +265,19 @@ aci_config = AciWebservice.deploy_configuration(cpu_cores = 1,
                                                 auth_enable=True)
 ```
 
-Then you can use the custom ACI configuration in deployment using the parent `WebService` class.
+Then you can use the custom ACI configuration in deployment using the `Model` class.
 
 ```python
-from azureml.core.webservice import Webservice
+from azureml.core.model import Model, InferenceConfig
+
 
-aci_service = Webservice.deploy_from_image(deployment_config=aci_config,
-                                           image=image,
-                                           name="aci_service_sample",
-                                           workspace=ws)
+inference_config = InferenceConfig(entry_script="score.py",
+                                   environment=myenv)
+aci_service = Model.deploy(workspace=ws,
+                       name="aci_service_sample",
+                       models=[model],
+                       inference_config=inference_config,
+                       deployment_config=aci_config)
 aci_service.wait_for_deployment(True)
 ```
 
@@ -315,4 +319,4 @@ print(token)
 ## Next steps
 
 * [Train and deploy an image classification model](tutorial-train-models-with-aml.md).
-* [Consume an Azure Machine Learning model deployed as a web service](how-to-consume-web-service.md).
+* [Consume an Azure Machine Learning model deployed as a web service](how-to-consume-web-service.md).