consistent pattern usage

Author: s-polly

articles/machine-learning/how-to-access-data-batch-endpoints-jobs.md

@@ -59,7 +59,7 @@ Use the Azure Machine Learning SDK for Python to sign in:
 from azure.ai.ml import MLClient
 from azure.identity import DefaultAzureCredential
 
-ml_client = MLClient.from_config(DefaultAzureCredential())
+ml_client = MLClient.from_config(credential=DefaultAzureCredential())
 ```
 
 If your configuration runs outside an Azure Machine Learning compute instance, you need to specify the workspace where the endpoint is deployed:

articles/machine-learning/how-to-package-models-app-service.md

@@ -87,7 +87,7 @@ Follow these steps to prepare your system.
     1. If you're running in an Azure Machine Learning compute instance, create an `MLClient` as follows:
     
         ```python
-        ml_client = MLClient.from_config(DefaultAzureCredential())
+        ml_client = MLClient.from_config(credential=DefaultAzureCredential())
         ```
     
         Otherwise, configure workspace details and get a handle to the workspace:

articles/machine-learning/how-to-package-models-moe.md

@@ -82,7 +82,7 @@ The workspace is the top-level resource for Azure Machine Learning, providing a
 2. If you're running in a compute instance in Azure Machine Learning, create an `MLClient` as follows:
 
     ```python
-    ml_client = MLClient.from_config(DefaultAzureCredential())
+    ml_client = MLClient.from_config(credential=DefaultAzureCredential())
     ```
 
     Otherwise, configure your workspace details and get a handle to the workspace:

articles/machine-learning/how-to-package-models.md

@@ -99,7 +99,7 @@ The workspace is the top-level resource for Azure Machine Learning, providing a
 2. If you're running in a compute instance in Azure Machine Learning, create an `MLClient` as follows:
 
     ```python
-    ml_client = MLClient.from_config(DefaultAzureCredential())
+    ml_client = MLClient.from_config(credential=DefaultAzureCredential())
     ```
 
     Otherwise, configure your workspace details and get a handle to the workspace:

articles/machine-learning/migrate-to-v2-command-job.md

@@ -61,7 +61,7 @@ This article gives a comparison of scenario(s) in SDK v1 and SDK v2.
     from azure.identity import DefaultAzureCredential
 
     #connect to the workspace
-    ml_client = MLClient.from_config(DefaultAzureCredential())
+    ml_client = MLClient.from_config(credential=DefaultAzureCredential())
 
     # set up pytorch environment
     env = Environment(

articles/machine-learning/migrate-to-v2-local-runs.md

@@ -57,7 +57,7 @@ This article gives a comparison of scenario(s) in SDK v1 and SDK v2.
     from azure.identity import DefaultAzureCredential
 
     #connect to the workspace
-    ml_client = MLClient.from_config(DefaultAzureCredential())
+    ml_client = MLClient.from_config(credential=DefaultAzureCredential())
 
     # set up pytorch environment
     env = Environment(