Finished everything

azuremlpythonsdk-v2/azml_03_realtime_inference.py

@@ -14,26 +14,28 @@ online_endpoint_name = ("srv-" + AZURE_WORKSPACE_NAME).lower()
 
 def main():
     # 1. Create or Load a ML client
-    ml_client = XXXX()
+    ml_client = create_or_load_ml_client()
 
     # 2. Create a endpoint
     print(f"Creating endpoint {online_endpoint_name}")
-    endpoint = XXXX(
+    endpoint = ManagedOnlineEndpoint(
         name=online_endpoint_name,
         auth_mode="key",
     )
 
     # Method `result()` should be added to wait until completion
-    ml_client.online_endpoints.XXXX(endpoint).result()
+    ml_client.online_endpoints.begin_create_or_update(endpoint).result()
 
     # 3. Create a deployment
-    best_model_latest_version = XXXX
+    best_model_latest_version = max(
+        [int(m.version) for m in ml_client.models.list(name=best_model_name)]
+    )
 
-    blue_deployment = XXXX(
+    blue_deployment = ManagedOnlineDeployment(
         name=online_endpoint_name,
         endpoint_name=online_endpoint_name,
         # @latest doesn't work with model paths
-        model=XXXX,
+        model=f"azureml:{best_model_name}:{best_model_latest_version}",
         instance_type=VM_SIZE,
         instance_count=1,
     )

azuremlpythonsdk-v2/azml_04_test_inference.py

@@ -7,11 +7,11 @@ from ml_client import create_or_load_ml_client
 
 def main():
     # 1. Load a Workspace
-    ml_client = XXXX()
+    ml_client = create_or_load_ml_client()
 
     # 2. Get predictions
-    output = ml_client.online_endpoints.XXXX(
-        endpoint_name=XXXX,
+    output = ml_client.online_endpoints.invoke(
+        endpoint_name=online_endpoint_name,
         deployment_name=online_endpoint_name,
         request_file="./diabetes_test_inference/request.json",
     )