Dharam dev

.github/workflows/workshop_ci.yml

@@ -32,7 +32,7 @@ jobs:
       - name: Install az ml & set default values for AML
         run: | #setup: provide group, workspace and location
           az extension add -n ml -y --version 2.2.1
-          az configure --defaults group=azureml workspace=ws01ent location=westus2   
+          az configure --defaults group=mlops-rg-910168 workspace=aml910168 location=eastus2   
       - name: run training and model validation
         run: |
          az ml job create -s -f src/workshop/core/pipelines/training_pipeline.yml

.github/workflows/workshop_unit_test.yml

@@ -1,4 +1,5 @@
 name: feature_engineering_unit_test
+
 on:
   workflow_dispatch:
   push:
@@ -31,7 +32,7 @@ jobs:
       - name: Install AZ ML and tools
         run: | # SETUP line 34 to point to your own AML workspace
           az extension add -n ml -y --version 2.2.1
-          az configure --defaults group=azureml workspace=ws01ent location=westus2   
+          az configure --defaults group=mlops-rg-910168 workspace=aml910168 location=eastus2   
       - name: Run Feature Engineering
         uses: ./.github/actions/aml-job-create
         with:

src/workshop/core/scoring/deployment.yml

@@ -1,6 +1,6 @@
 $schema: https://azuremlschemas.azureedge.net/latest/managedOnlineDeployment.schema.json
 name: green
-endpoint_name: mlops-workshop-endpoint #setup replace `mlops-workshop-endpoint` with your own endpoint name defined in endpoint.yml
+endpoint_name: mlops-workshop-endpointD #setup replace `mlops-workshop-endpoint` with your own endpoint name defined in endpoint.yml
 model: azureml:nyc_fare_prediction:1
 code_configuration:
   code: ./

src/workshop/core/scoring/endpoint.yml

@@ -1,3 +1,3 @@
 $schema: https://azuremlschemas.azureedge.net/latest/managedOnlineEndpoint.schema.json
-name: mlops-workshop-endpoint #setup replace `mlops-workshop-endpoint` with your own endpoint name. It has to be globally unique
+name: mlops-workshop-endpointD #setup replace `mlops-workshop-endpoint` with your own endpoint name. It has to be globally unique
 auth_mode: key

src/workshop/core/training/ml_training.py

@@ -43,7 +43,7 @@ def createClassModel(algo_name, catg, nums):
         #---------------------------------------------
         #setup: Update alpha value
         #---------------------------------------------
-        model = Ridge(alpha=100000)  #setup
+        model = Ridge(alpha=100)  #setup
     elif algo_name == 'random_forest':
         model = RandomForestRegressor()
     else:
@@ -100,4 +100,4 @@ def main(args):
     # parse args
     args = parse_args()
     # run main function
-    main(args)
+    main(args)