Merge pull request #104 from Azure/feature/rai-aml-cli-v2

fixes github actions as RAI has not been implemented yet for GHA

.github/PULL_REQUEST_TEMPLATE.md

@@ -1,4 +1,4 @@
-# PR into Azure/mlops-v2
+# PR into Azure/mlops-project-template
 
 ## Checklist
 

classical/aml-cli-v2/data-science/environment/train-conda.yml

@@ -12,5 +12,8 @@ dependencies:
       - pandas==1.2.1
       - joblib==1.0.0
       - matplotlib==3.3.3
-      - git+https://github.com/microsoft/AzureML-Observability#subdirectory=aml-obs-client
-      - git+https://github.com/microsoft/AzureML-Observability#subdirectory=aml-obs-collector
+      - fairlearn==0.7.0
+      - azureml-contrib-fairness==1.38.0
+      - interpret-community==0.24.1
+      - interpret-core==0.2.7
+      - azureml-interpret==1.38.0

classical/aml-cli-v2/data-science/environment/train-monitor-conda.yml

@@ -12,5 +12,8 @@ dependencies:
       - pandas==1.2.1
       - joblib==1.0.0
       - matplotlib==3.3.3
-      - git+https://github.com/microsoft/AzureML-Observability#subdirectory=aml-obs-client
-      - git+https://github.com/microsoft/AzureML-Observability#subdirectory=aml-obs-collector
+      - fairlearn==0.7.0
+      - azureml-contrib-fairness==1.38.0
+      - interpret-community==0.24.1
+      - interpret-core==0.2.7
+      - azureml-interpret==1.38.0

classical/aml-cli-v2/data-science/src/evaluate.py

@@ -0,0 +1,233 @@
+import argparse
+from pathlib import Path
+import os
+import pickle
+
+import numpy as np
+import pandas as pd
+from matplotlib import pyplot as plt
+
+from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error
+
+from azureml.core import Run, Model
+
+from fairlearn.metrics._group_metric_set import _create_group_metric_set
+from azureml.contrib.fairness import upload_dashboard_dictionary, download_dashboard_by_upload_id
+
+from interpret_community import TabularExplainer
+from azureml.interpret import ExplanationClient
+
+import mlflow
+import mlflow.sklearn
+
+# current run 
+run = Run.get_context()
+ws = run.experiment.workspace
+
+TARGET_COL = "cost"
+
+NUMERIC_COLS = [
+    "distance",
+    "dropoff_latitude",
+    "dropoff_longitude",
+    "passengers",
+    "pickup_latitude",
+    "pickup_longitude",
+    "pickup_weekday",
+    "pickup_month",
+    "pickup_monthday",
+    "pickup_hour",
+    "pickup_minute",
+    "pickup_second",
+    "dropoff_weekday",
+    "dropoff_month",
+    "dropoff_monthday",
+    "dropoff_hour",
+    "dropoff_minute",
+    "dropoff_second",
+]
+
+CAT_NOM_COLS = [
+    "store_forward",
+    "vendor",
+]
+
+CAT_ORD_COLS = [
+]
+
+SENSITIVE_COLS = ["vendor"] # for fairlearn dashborad
+
+
+def parse_args():
+        
+    parser = argparse.ArgumentParser("predict")
+    parser.add_argument("--model_name", type=str, help="Name of registered model")
+    parser.add_argument("--model_input", type=str, help="Path of input model")
+    parser.add_argument("--prepared_data", type=str, help="Path to transformed data")
+    parser.add_argument("--predictions", type=str, help="Path of predictions")
+    parser.add_argument("--score_report", type=str, help="Path to score report")
+    parser.add_argument('--deploy_flag', type=str, help='A deploy flag whether to deploy or no')
+
+    args = parser.parse_args()
+
+    return args
+
+
+def main():
+
+    args = parse_args()
+
+    lines = [
+        f"Model path: {args.model_input}",
+        f"Test data path: {args.prepared_data}",
+        f"Predictions path: {args.predictions}",
+        f"Scoring output path: {args.score_report}",
+    ]
+
+    for line in lines:
+        print(line)
+
+    # ---------------- Model Evaluation ---------------- #
+
+    # Load the test data
+
+    print("mounted_path files: ")
+    arr = os.listdir(args.prepared_data)
+
+    train_data = pd.read_csv((Path(args.prepared_data) / "train.csv"))
+    test_data = pd.read_csv((Path(args.prepared_data) / "test.csv"))
+
+    y_train = train_data[TARGET_COL]
+    X_train = train_data[NUMERIC_COLS + CAT_NOM_COLS + CAT_ORD_COLS]
+
+    y_test = test_data[TARGET_COL]
+    X_test = test_data[NUMERIC_COLS + CAT_NOM_COLS + CAT_ORD_COLS]
+
+    # Load the model from input port
+    model = pickle.load(open((Path(args.model_input) / "model.pkl"), "rb"))
+
+    # Get predictions to y_test (y_test)
+    yhat_test = model.predict(X_test)
+
+    # Save the output data with feature columns, predicted cost, and actual cost in csv file
+    output_data = X_test.copy()
+    output_data["real_label"] = y_test
+    output_data["predicted_label"] = yhat_test
+    output_data.to_csv((Path(args.predictions) / "predictions.csv"))
+
+    # Evaluate Model performance with the test set
+    r2 = r2_score(y_test, yhat_test)
+    mse = mean_squared_error(y_test, yhat_test)
+    rmse = np.sqrt(mse)
+    mae = mean_absolute_error(y_test, yhat_test)
+
+    # Print score report to a text file
+    (Path(args.score_report) / "score.txt").write_text(
+        "Scored with the following model:\n{}".format(model)
+    )
+    with open((Path(args.score_report) / "score.txt"), "a") as f:
+        f.write("Mean squared error: %.2f \n" % mse)
+        f.write("Root mean squared error: %.2f \n" % rmse)
+        f.write("Mean absolute error: %.2f \n" % mae)
+        f.write("Coefficient of determination: %.2f \n" % r2)
+
+
+    mlflow.log_metric("test r2", r2)
+    mlflow.log_metric("test mse", mse)
+    mlflow.log_metric("test rmse", rmse)
+    mlflow.log_metric("test mae", mae)
+
+    # Visualize results
+    plt.scatter(y_test, yhat_test,  color='black')
+    plt.plot(y_test, y_test, color='blue', linewidth=3)
+    plt.xlabel("Real value")
+    plt.ylabel("Predicted value")
+    plt.title("Comparing Model Predictions to Real values - Test Data")
+    plt.savefig("predictions.png")
+    mlflow.log_artifact("predictions.png")
+
+    # -------------------- Promotion ------------------- #
+    scores = {}
+    predictions = {}
+    score = r2_score(y_test, yhat_test) # current model
+    for model_run in Model.list(ws):
+        if model_run.name == args.model_name:
+            model_path = Model.download(model_run, exist_ok=True)
+            mdl = pickle.load(open((Path(model_path) / "model.pkl"), "rb"))
+            predictions[model_run.id] = mdl.predict(X_test)
+            scores[model_run.id] = r2_score(y_test, predictions[model_run.id])
+        
+    print(scores)
+    if scores:
+        if score >= max(list(scores.values())):
+            deploy_flag = 1
+        else:
+            deploy_flag = 0
+    else:
+        deploy_flag = 1
+    print("Deploy flag: ",deploy_flag)
+
+    with open((Path(args.deploy_flag) / "deploy_flag"), 'w') as f:
+        f.write('%d' % int(deploy_flag))
+                
+    scores["current model"] = score
+    perf_comparison_plot = pd.DataFrame(scores, index=["r2 score"]).plot(kind='bar', figsize=(15, 10))
+    perf_comparison_plot.figure.savefig("perf_comparison.png")
+    perf_comparison_plot.figure.savefig(Path(args.score_report) / "perf_comparison.png")
+    
+    mlflow.log_metric("deploy flag", bool(deploy_flag))
+    mlflow.log_artifact("perf_comparison.png")
+    
+
+    # -------------------- FAIRNESS ------------------- #
+    # Calculate Fairness Metrics over Sensitive Features
+    # Create a dictionary of model(s) you want to assess for fairness 
+    
+    sf = { col: X_test[[col]] for col in SENSITIVE_COLS }
+    predictions["currrent model"] = [x for x in model.predict(X_test)]
+    
+    dash_dict_all = _create_group_metric_set(y_true=y_test,
+                                             predictions=predictions,
+                                             sensitive_features=sf,
+                                             prediction_type='regression',
+                                            )
+    
+    # Upload the dashboard to Azure Machine Learning
+    dashboard_title = "Fairness insights Comparison of Models"
+
+    # Set validate_model_ids parameter of upload_dashboard_dictionary to False 
+    # if you have not registered your model(s)
+    upload_id = upload_dashboard_dictionary(run,
+                                            dash_dict_all,
+                                            dashboard_name=dashboard_title,
+                                            validate_model_ids=False)
+    print("\nUploaded to id: {0}\n".format(upload_id))
+
+    
+    # -------------------- Explainability ------------------- #
+    tabular_explainer = TabularExplainer(model,
+                                   initialization_examples=X_train,
+                                   features=X_train.columns)
+    
+    # save explainer                                 
+    #joblib.dump(tabular_explainer, os.path.join(tabular_explainer, "explainer"))
+
+    # find global explanations for feature importance
+    # you can use the training data or the test data here, 
+    # but test data would allow you to use Explanation Exploration
+    global_explanation = tabular_explainer.explain_global(X_test)
+
+    # sorted feature importance values and feature names
+    sorted_global_importance_values = global_explanation.get_ranked_global_values()
+    sorted_global_importance_names = global_explanation.get_ranked_global_names()
+
+    print("Explainability feature importance:")
+    # alternatively, you can print out a dictionary that holds the top K feature names and values
+    global_explanation.get_feature_importance_dict()
+    
+    client = ExplanationClient.from_run(run)
+    client.upload_model_explanation(global_explanation, comment='global explanation: all features')
+
+
+if __name__ == "__main__":
+    main()

classical/aml-cli-v2/data-science/src/prep.py

@@ -0,0 +1,78 @@
+import argparse
+
+from pathlib import Path
+import os
+import numpy as np
+import pandas as pd
+
+import mlflow
+
+
+def parse_args():
+
+    parser = argparse.ArgumentParser("prep")
+    parser.add_argument("--raw_data", type=str, help="Path to raw data")
+    parser.add_argument("--prepared_data", type=str, help="Path of prepared data")
+    parser.add_argument("--enable_monitoring", type=str, help="enable logging to ADX")
+    parser.add_argument("--table_name", type=str, default="mlmonitoring", help="Table name in ADX for logging")
+    args = parser.parse_args()
+
+    return args
+
+def log_training_data(df, table_name):
+    from obs.collector import Online_Collector
+    collector = Online_Collector(table_name)
+    collector.batch_collect(df)
+
+def main():
+
+    # ---------- Parse Arguments ----------- #
+    # -------------------------------------- #
+
+    args = parse_args()
+
+    lines = [
+        f"Raw data path: {args.raw_data}",
+        f"Data output path: {args.prepared_data}",
+    ]
+
+    for line in lines:
+        print(line)
+
+    # ------------ Reading Data ------------ #
+    # -------------------------------------- #
+
+    print("mounted_path files: ")
+    arr = os.listdir(args.raw_data)
+    print(arr)
+
+    data = pd.read_csv((Path(args.raw_data) / 'taxi-data.csv'))
+
+    # ------------- Split Data ------------- #
+    # -------------------------------------- #
+
+    # Split data into train, val and test datasets
+
+    random_data = np.random.rand(len(data))
+
+    msk_train = random_data < 0.7
+    msk_val = (random_data >= 0.7) & (random_data < 0.85)
+    msk_test = random_data >= 0.85
+
+    train = data[msk_train]
+    val = data[msk_val]
+    test = data[msk_test] 
+
+    mlflow.log_metric('train size', train.shape[0])
+    mlflow.log_metric('val size', val.shape[0])
+    mlflow.log_metric('test size', test.shape[0])
+
+    train.to_csv((Path(args.prepared_data) / "train.csv"))
+    val.to_csv((Path(args.prepared_data) / "val.csv"))
+    test.to_csv((Path(args.prepared_data) / "test.csv"))
+
+    if (args.enable_monitoring.lower == 'true' or args.enable_monitoring == '1' or args.enable_monitoring.lower == 'yes'):
+        log_training_data(data, args.table_name)
+
+if __name__ == "__main__":
+    main()

classical/aml-cli-v2/data-science/src/register.py

@@ -0,0 +1,56 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+import argparse
+from pathlib import Path
+import pickle
+
+import mlflow
+
+from azureml.core import Run
+
+# Get run
+run = Run.get_context()
+run_id = run.get_details()["runId"]
+print(run_id)
+
+def parse_args():
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--model_name', type=str, help='Name under which model will be registered')
+    parser.add_argument('--model_path', type=str, help='Model directory')
+    parser.add_argument('--deploy_flag', type=str, help='A deploy flag whether to deploy or no')
+    
+    args, _ = parser.parse_known_args()
+    print(f'Arguments: {args}')
+
+    return args
+
+
+def main():
+
+    args = parse_args()
+
+    model_name = args.model_name
+    model_path = args.model_path
+
+    with open((Path(args.deploy_flag) / "deploy_flag"), 'rb') as f:
+        deploy_flag = int(f.read())
+
+    if deploy_flag==1:
+        
+        print("Registering ", model_name)
+
+        model = pickle.load(open((Path(model_path) / "model.pkl"), "rb"))
+        # log model using mlflow 
+        mlflow.sklearn.log_model(model, model_name)
+
+        # register model using mlflow model
+        model_uri = f'runs:/{run_id}/{args.model_name}'
+        mlflow.register_model(model_uri, model_name)
+        
+    else:
+        print("Model will not be registered!")
+
+if __name__ == "__main__":
+    main()

classical/aml-cli-v2/data-science/src/train.py

@@ -0,0 +1,191 @@
+
+import argparse
+
+from pathlib import Path
+import os
+import pickle
+
+import numpy as np
+import pandas as pd
+from matplotlib import pyplot as plt
+
+from sklearn.pipeline import Pipeline
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.impute import SimpleImputer
+from sklearn.preprocessing import StandardScaler, MinMaxScaler
+from sklearn.preprocessing import OneHotEncoder
+from sklearn.compose import ColumnTransformer
+from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error
+
+import mlflow
+import mlflow.sklearn
+
+TARGET_COL = "cost"
+
+NUMERIC_COLS = [
+    "distance",
+    "dropoff_latitude",
+    "dropoff_longitude",
+    "passengers",
+    "pickup_latitude",
+    "pickup_longitude",
+    "pickup_weekday",
+    "pickup_month",
+    "pickup_monthday",
+    "pickup_hour",
+    "pickup_minute",
+    "pickup_second",
+    "dropoff_weekday",
+    "dropoff_month",
+    "dropoff_monthday",
+    "dropoff_hour",
+    "dropoff_minute",
+    "dropoff_second",
+]
+
+CAT_NOM_COLS = [
+    "store_forward",
+    "vendor",
+]
+
+CAT_ORD_COLS = [
+]
+
+
+def parse_args():
+
+    parser = argparse.ArgumentParser("train")
+    parser.add_argument("--prepared_data", type=str, help="Path to training data")
+    parser.add_argument("--model_output", type=str, help="Path of output model")
+
+    # classifier specific arguments
+    parser.add_argument('--regressor__n_estimators', type=int, default=500,
+                        help='Number of trees')
+    parser.add_argument('--regressor__bootstrap', type=int, default=1,
+                        help='Method of selecting samples for training each tree')   
+    parser.add_argument('--regressor__max_depth', type=int, default=10,
+                        help=' Maximum number of levels in tree')
+    parser.add_argument('--regressor__max_features', type=str, default='auto',
+                        help='Number of features to consider at every split')    
+    parser.add_argument('--regressor__min_samples_leaf', type=int, default=4,
+                        help='Minimum number of samples required at each leaf node')    
+    parser.add_argument('--regressor__min_samples_split', type=int, default=5,
+                        help='Minimum number of samples required to split a node')
+
+    args = parser.parse_args()
+
+    return args
+
+def main():
+
+    args = parse_args()
+    
+    lines = [
+        f"Training data path: {args.prepared_data}",
+        f"Model output path: {args.model_output}",
+    ]
+
+    for line in lines:
+        print(line)
+
+    print("mounted_path files: ")
+    arr = os.listdir(args.prepared_data)
+    print(arr)
+
+    train_data = pd.read_csv((Path(args.prepared_data) / "train.csv"))
+
+    # Split the data into input(X) and output(y)
+    y_train = train_data[TARGET_COL]
+    X_train = train_data[NUMERIC_COLS + CAT_NOM_COLS + CAT_ORD_COLS]
+
+    # Train a Linear Regression Model with the train set
+
+    # numerical features
+    numeric_transformer = Pipeline(steps=[
+        ('standardscaler', StandardScaler())])
+
+    # ordinal features transformer
+    ordinal_transformer = Pipeline(steps=[
+        ('imputer', SimpleImputer(missing_values=np.nan, strategy="most_frequent")),
+        ('minmaxscaler', MinMaxScaler())
+    ])
+
+    # nominal features transformer
+    nominal_transformer = Pipeline(steps=[
+        ('imputer', SimpleImputer(missing_values=np.nan, strategy="most_frequent")),
+        ('onehot', OneHotEncoder(sparse=False))
+    ])
+
+    # imputer only for all other features
+    imputer_transformer = Pipeline(steps=[
+        ('imputer', SimpleImputer(missing_values=np.nan, strategy="most_frequent"))
+    ])
+
+    # preprocessing pipeline
+    preprocessor = ColumnTransformer(
+        transformers=[
+            ('numeric', numeric_transformer, NUMERIC_COLS),
+           #('ordinal', ordinal_transformer, CAT_ORD_COLS),
+            ('nominal', nominal_transformer, CAT_NOM_COLS)], # other features are already binary
+            remainder="drop")
+
+    # append regressor to preprocessing pipeline.
+    # now we have a full prediction pipeline.
+    
+    #model = Pipeline(steps=[('preprocessor', preprocessor),
+    #                      ('regressor', RandomForestRegressor(
+    #                          n_estimators = args.regressor__n_estimators,
+    #                          bootstrap = args.regressor__bootstrap,
+    #                          max_depth = args.regressor__max_depth,
+    #                          max_features = args.regressor__max_features,
+    #                          min_samples_leaf = args.regressor__min_samples_leaf,
+    #                          min_samples_split = args.regressor__min_samples_split,
+    #                          random_state=0))])
+
+
+    model = RandomForestRegressor(n_estimators = args.regressor__n_estimators,
+                                  bootstrap = args.regressor__bootstrap,
+                                  max_depth = args.regressor__max_depth,
+                                  max_features = args.regressor__max_features,
+                                  min_samples_leaf = args.regressor__min_samples_leaf,
+                                  min_samples_split = args.regressor__min_samples_split,
+                                  random_state=0)
+
+    mlflow.log_param("model", "RandomForestRegressor")
+    mlflow.log_param("n_estimators", args.regressor__n_estimators)
+    mlflow.log_param("bootstrap", args.regressor__bootstrap)
+    mlflow.log_param("max_depth", args.regressor__max_depth)
+    mlflow.log_param("max_features", args.regressor__max_features)
+    mlflow.log_param("min_samples_leaf", args.regressor__min_samples_leaf)
+    mlflow.log_param("min_samples_split", args.regressor__min_samples_split)
+
+    model.fit(X_train, y_train)
+
+    # Predict using the Regression Model
+    yhat_train = model.predict(X_train)
+
+    # Evaluate Regression performance with the train set
+    r2 = r2_score(y_train, yhat_train)
+    mse = mean_squared_error(y_train, yhat_train)
+    rmse = np.sqrt(mse)
+    mae = mean_absolute_error(y_train, yhat_train)
+
+    mlflow.log_metric("train r2", r2)
+    mlflow.log_metric("train mse", mse)
+    mlflow.log_metric("train rmse", rmse)
+    mlflow.log_metric("train mae", mae)
+
+    # Visualize results
+    plt.scatter(y_train, yhat_train,  color='black')
+    plt.plot(y_train, y_train, color='blue', linewidth=3)
+    plt.xlabel("Real value")
+    plt.ylabel("Predicted value")
+    plt.savefig("regression_results.png")
+    mlflow.log_artifact("regression_results.png")
+
+    # Save the model
+    pickle.dump(model, open((Path(args.model_output) / "model.pkl"), "wb"))
+
+if __name__ == "__main__":
+    main()
+    

classical/aml-cli-v2/mlops/azureml/train/pipeline.yml

@@ -1,23 +1,21 @@
 $schema: https://azuremlschemas.azureedge.net/latest/pipelineJob.schema.json
 type: pipeline
-experiment_name: taxi-fare-training
+experiment_name: taxi-fare-training-gha
 description: Training Pipeline to train a model that predicts taxi fare price
 
 # <inputs_and_outputs>
 inputs:
-  input: #using local data, will create an anonymous data asset
+  input: #using local data, will crate an anonymous data asset
     type: uri_folder
     path: ../../../data/
-  enable_monitoring: "true"
-  table_name: 'taximonitoring'
+  enable_monitoring: 'false'
 
 outputs: 
-  train_data:
-  val_data:
-  test_data:
+  prepared_data:
   trained_model:
-  evaluation_output:
-  model_info_output_path:
+  predictions:
+  score_report:
+  deploy_flag:
 # </inputs_and_outputs>
 
 # <jobs>
@@ -30,137 +28,68 @@ jobs:
   prep_data:
     name: prep_data
     display_name: prep-data
-    code: ../../../data-science/src/prep
+    code: ../../../data-science/src
     command: >-
       python prep.py 
       --raw_data ${{inputs.raw_data}} 
-      --train_data ${{outputs.train_data}}
-      --val_data ${{outputs.val_data}}
-      --test_data ${{outputs.test_data}}
+      --prepared_data ${{outputs.prepared_data}}
       --enable_monitoring ${{inputs.enable_monitoring}}
-      --table_name ${{inputs.table_name}}
     environment: azureml:taxi-train-env@latest
     inputs:
       raw_data: ${{parent.inputs.input}}
       enable_monitoring: ${{parent.inputs.enable_monitoring}}
-      table_name: ${{parent.inputs.table_name}}
     outputs:
-      train_data: ${{parent.outputs.train_data}}
-      val_data: ${{parent.outputs.val_data}}
-      test_data: ${{parent.outputs.test_data}}
+      prepared_data: ${{parent.outputs.prepared_data}}
 
   train_model:
     name: train_model
     display_name: train-model
-    code: ../../../data-science/src/train
+    code: ../../../data-science/src
     command: >-
       python train.py 
-      --train_data ${{inputs.train_data}} 
+      --prepared_data ${{inputs.prepared_data}} 
       --model_output ${{outputs.model_output}}
     environment: azureml:taxi-train-env@latest
     inputs:
-      train_data: ${{parent.jobs.prep_data.outputs.train_data}}
+      prepared_data: ${{parent.jobs.prep_data.outputs.prepared_data}}
     outputs:
       model_output: ${{parent.outputs.trained_model}}
   
   evaluate_model:
     name: evaluate_model
     display_name: evaluate-model
-    code: ../../../data-science/src/evaluate
+    code: ../../../data-science/src
     command: >-
       python evaluate.py 
       --model_name ${{inputs.model_name}} 
       --model_input ${{inputs.model_input}} 
-      --test_data ${{inputs.test_data}} 
-      --evaluation_output ${{outputs.evaluation_output}}
+      --prepared_data ${{inputs.prepared_data}}
+      --predictions ${{outputs.predictions}}
+      --score_report ${{outputs.score_report}}
+      --deploy_flag ${{outputs.deploy_flag}}
     environment: azureml:taxi-train-env@latest
     inputs:
-      model_name: "taxi-model"
+      model_name: "taxi-model-gha"
       model_input: ${{parent.jobs.train_model.outputs.model_output}}
-      test_data: ${{parent.jobs.prep_data.outputs.test_data}}
+      prepared_data: ${{parent.jobs.prep_data.outputs.prepared_data}}
     outputs:
-      evaluation_output: ${{parent.outputs.evaluation_output}}
+      predictions: ${{parent.outputs.predictions}}
+      score_report: ${{parent.outputs.score_report}}
+      deploy_flag: ${{parent.outputs.deploy_flag}}
 
   register_model:
     name: register_model
     display_name: register-model
-    code: ../../../data-science/src/register
+    code: ../../../data-science/src
     command: >-
       python register.py 
       --model_name ${{inputs.model_name}} 
       --model_path ${{inputs.model_path}} 
-      --evaluation_output ${{inputs.evaluation_output}}
-      --model_info_output_path ${{outputs.model_info_output_path}}
+      --deploy_flag ${{inputs.deploy_flag}}
     environment: azureml:taxi-train-env@latest
     inputs:
-      model_name: "taxi-model"
+      model_name: "taxi-model-gha"
       model_path: ${{parent.jobs.train_model.outputs.model_output}}
-      evaluation_output: ${{parent.jobs.evaluate_model.outputs.evaluation_output}}
-    outputs:
-      model_info_output_path: ${{parent.outputs.model_info_output_path}}
+      deploy_flag: ${{parent.jobs.evaluate_model.outputs.deploy_flag}}
 
-  create_rai_job:
-    type: command
-    component: azureml:rai_insights_constructor@latest
-    limits:
-      timeout: 120
-    inputs:
-      title: Responsible AI for Taxi Fare Prediction
-      task_type: regression
-      model_info_path: ${{parent.jobs.register_model.outputs.model_info_output_path}}
-      train_dataset: ${{parent.jobs.prep_data.outputs.train_data}}
-      test_dataset: ${{parent.jobs.prep_data.outputs.test_data}}
-      target_column_name: "cost"
-      categorical_column_names: '["store_forward", "vendor"]'
-
-  explain_01:
-    type: command
-    component: azureml:rai_insights_explanation@latest
-    limits:
-      timeout: 120
-    inputs:
-      comment: Some random string
-      rai_insights_dashboard: ${{parent.jobs.create_rai_job.outputs.rai_insights_dashboard}}
-
-  causal_01:
-    type: command
-    component: azureml:rai_insights_causal@latest
-    limits:
-      timeout: 120
-    inputs:
-      rai_insights_dashboard: ${{parent.jobs.create_rai_job.outputs.rai_insights_dashboard}}
-      treatment_features: '["distance", "passengers"]'
-      heterogeneity_features: '["store_forward", "vendor"]'
-
-  counterfactual_01:
-    type: command
-    component: azureml:rai_insights_counterfactual@latest
-    limits:
-      timeout: 600
-    inputs:
-      rai_insights_dashboard: ${{parent.jobs.create_rai_job.outputs.rai_insights_dashboard}}
-      total_CFs: 10
-      desired_range: '[16, 30]'
-      feature_importance: True
-
-  error_analysis_01:
-    type: command
-    component: azureml:rai_insights_erroranalysis@latest
-    limits:
-      timeout: 120
-    inputs:
-      rai_insights_dashboard: ${{parent.jobs.create_rai_job.outputs.rai_insights_dashboard}}
-      filter_features: '["distance", "passengers"]'
-
-  gather_01:
-    type: command
-    component: azureml:rai_insights_gather@latest
-    limits:
-      timeout: 120
-    inputs:
-      constructor: ${{parent.jobs.create_rai_job.outputs.rai_insights_dashboard}}
-      insight_1: ${{parent.jobs.causal_01.outputs.causal}}
-      insight_2: ${{parent.jobs.counterfactual_01.outputs.counterfactual}}
-      insight_3: ${{parent.jobs.error_analysis_01.outputs.error_analysis}}
-      insight_4: ${{parent.jobs.explain_01.outputs.explanation}}
-# </jobs>
+# </jobs>

classical/aml-cli-v2/mlops/devops-pipelines/deploy-batch-endpoint-pipeline.yml

@@ -11,7 +11,7 @@ variables:
 - name: version
   value: aml-cli-v2 
 - name: endpoint_name
-  value: taxi-batch-$(namespace)$(postfix)$(environment)
+  value: taxi2-batch-$(namespace)$(postfix)$(environment)
 - name: endpoint_type
   value: batch
 
@@ -28,7 +28,7 @@ resources:
       name: Azure/mlops-templates # need to change org name from "Azure" to your own org
       endpoint: github-connection # need to set up and hardcode
       type: github
-      ref: main
+      ref: main-jul31
       
 
 stages:

classical/aml-cli-v2/mlops/devops-pipelines/deploy-model-training-pipeline.yml

@@ -37,13 +37,11 @@ stages:
   displayName: Deploy Training Pipeline
   jobs:
     - job: DeployTrainingPipeline
-      timeoutInMinutes: 120 # how long to run the job before automatically cancelling
       steps:
       - checkout: self
         path: s/
       - checkout: mlops-templates
         path: s/templates/
-      - template: templates/tests/unit-tests.yml@mlops-templates
       - template: templates/${{ variables.version }}/install-az-cli.yml@mlops-templates
       - template: templates/${{ variables.version }}/install-aml-cli.yml@mlops-templates
       - template: templates/${{ variables.version }}/connect-to-workspace.yml@mlops-templates
@@ -53,16 +51,9 @@ stages:
           environment_name: taxi-train-env
           environment_file: mlops/azureml/train/train-env.yml
           enable_monitoring: $(enable_monitoring)
-      - checkout: rai-vnext-preview
-        path: s/
-      - template: register-rai-components.yml
-      - checkout: self
-        path: s/
-      - checkout: mlops-templates
-        path: s/templates/
       - template: templates/${{ variables.version }}/run-pipeline.yml@mlops-templates
         parameters: 
           pipeline_file: mlops/azureml/train/pipeline.yml
           experiment_name: $(environment)_taxi_fare_train_$(Build.SourceBranchName)
           display_name: $(environment)_taxi_fare_run_$(Build.BuildID)
-          enable_monitoring: $(enable_monitoring)
+          enable_monitoring: $(enable_monitoring)

classical/aml-cli-v2/mlops/devops-pipelines/deploy-online-endpoint-pipeline.yml

@@ -7,11 +7,11 @@ variables:
     - template: ../../config-infra-prod.yml
 - ${{ if ne(variables['Build.SourceBranchName'], 'main') }}:  
     # 'develop' or feature branches: DEV environment
-    - template: ../../config-infra-dev.yml
+    - template: ../../../../config-infra-dev.yml
 - name: version
   value: aml-cli-v2 
 - name: endpoint_name
-  value: taxi-online-$(namespace)$(postfix)$(environment)
+  value: taxi2-online-$(namespace)$(postfix)$(environment)
 - name: endpoint_type
   value: online
 
@@ -29,7 +29,7 @@ resources:
       name: Azure/mlops-templates # need to change org name from "Azure" to your own org
       endpoint: github-connection # need to set up and hardcode
       type: github
-      ref: main
+      ref: main-jul31
 
 stages:
 - stage: CreateOnlineEndpoint

classical/aml-cli-v2/mlops/github-actions/deploy-batch-endpoint-pipeline-classical.yml

@@ -26,7 +26,7 @@ jobs:
         resource_group: ${{ needs.get-config.outputs.resource_group }}
         workspace_name: ${{ needs.get-config.outputs.aml_workspace }} 
         endpoint_file: mlops/azureml/deploy/batch/batch-endpoint.yml
-        endpoint_name:  ${{ format('taxi-batch-{0}', needs.get-config.outputs.bep) }} 
+        endpoint_name:  ${{ format('taxi-gha-{0}', needs.get-config.outputs.bep) }} 
         endpoint_type: batch
       secrets:
         creds: ${{secrets.AZURE_CREDENTIALS}}
@@ -37,7 +37,7 @@ jobs:
         resource_group: ${{ needs.get-config.outputs.resource_group }}
         workspace_name: ${{ needs.get-config.outputs.aml_workspace }} 
         endpoint_file: mlops/azureml/deploy/batch/batch-deployment.yml
-        endpoint_name: ${{ format('taxi-batch-{0}', needs.get-config.outputs.bep) }} 
+        endpoint_name: ${{ format('taxi-gha-{0}', needs.get-config.outputs.bep) }} 
         endpoint_type: batch
         deployment_name: eptestdeploy
       secrets:

classical/aml-cli-v2/mlops/github-actions/deploy-online-endpoint-pipeline-classical.yml

@@ -14,7 +14,7 @@ jobs:
         resource_group: ${{ needs.get-config.outputs.resource_group }}
         workspace_name: ${{ needs.get-config.outputs.aml_workspace }} 
         endpoint_file: mlops/azureml/deploy/online/online-endpoint.yml
-        endpoint_name: ${{ format('taxi-online-{0}', needs.get-config.outputs.oep) }}
+        endpoint_name: ${{ format('taxi-gha-{0}', needs.get-config.outputs.oep) }}
         endpoint_type: online
       secrets:
         creds: ${{secrets.AZURE_CREDENTIALS}}
@@ -25,7 +25,7 @@ jobs:
         resource_group: ${{ needs.get-config.outputs.resource_group }}
         workspace_name: ${{ needs.get-config.outputs.aml_workspace }} 
         endpoint_file: mlops/azureml/deploy/online/online-deployment.yml
-        endpoint_name: ${{ format('taxi-online-{0}', needs.get-config.outputs.oep) }}
+        endpoint_name: ${{ format('taxi-gha-{0}', needs.get-config.outputs.oep) }}
         endpoint_type: online
         deployment_name: taxi-online-dp
       secrets:
@@ -37,6 +37,6 @@ jobs:
         resource_group: ${{ needs.get-config.outputs.resource_group }}
         workspace_name: ${{ needs.get-config.outputs.aml_workspace }} 
         traffic_allocation: taxi-online-dp=100
-        endpoint_name: ${{ format('taxi-online-{0}', needs.get-config.outputs.oep) }}
+        endpoint_name: ${{ format('taxi-gha-{0}', needs.get-config.outputs.oep) }}
       secrets:
         creds: ${{secrets.AZURE_CREDENTIALS}}

classical/rai-aml-cli-v2/data-science/environment/train-conda.yml

@@ -0,0 +1,16 @@
+channels:
+  - defaults
+  - anaconda
+  - conda-forge
+dependencies:
+  - python=3.7.5
+  - pip
+  - pip:
+      - azureml-mlflow==1.38.0
+      - azureml-sdk==1.38.0
+      - scikit-learn==0.24.1
+      - pandas==1.2.1
+      - joblib==1.0.0
+      - matplotlib==3.3.3
+      - git+https://github.com/microsoft/AzureML-Observability#subdirectory=aml-obs-client
+      - git+https://github.com/microsoft/AzureML-Observability#subdirectory=aml-obs-collector

classical/rai-aml-cli-v2/data-science/environment/train-monitor-conda.yml

@@ -0,0 +1,16 @@
+channels:
+  - defaults
+  - anaconda
+  - conda-forge
+dependencies:
+  - python=3.7.5
+  - pip
+  - pip:
+      - azureml-mlflow==1.38.0
+      - azureml-sdk==1.38.0
+      - scikit-learn==0.24.1
+      - pandas==1.2.1
+      - joblib==1.0.0
+      - matplotlib==3.3.3
+      - git+https://github.com/microsoft/AzureML-Observability#subdirectory=aml-obs-client
+      - git+https://github.com/microsoft/AzureML-Observability#subdirectory=aml-obs-collector

classical/rai-aml-cli-v2/data/taxi-request.json

@@ -0,0 +1,3 @@
+{"input_data": [[2.86,40.66551971,-73.98258972,1,40.69801331,-73.97357178,0,2,1,1,19,21,3,56,1,1,19,21,21,57],
+                [3.98,40.68072128,-73.931633,1,40.6909523,-73.99185181,0,2,0,1,4,21,44,11,0,1,4,21,59,35]]
+}

classical/rai-aml-cli-v2/mlops/azureml/deploy/batch/batch-deployment.yml

@@ -0,0 +1,16 @@
+$schema: https://azuremlschemas.azureedge.net/latest/batchDeployment.schema.json
+name: batch-dp
+endpoint_name: taxi-fare-batch
+model: azureml:taxi-model@latest
+compute: azureml:batch-cluster
+resources:
+  instance_count: 1
+max_concurrency_per_instance: 2
+mini_batch_size: 10
+output_action: append_row
+output_file_name: predictions.csv
+retry_settings:
+  max_retries: 3
+  timeout: 30
+error_threshold: -1
+logging_level: info

classical/rai-aml-cli-v2/mlops/azureml/deploy/batch/batch-endpoint.yml

@@ -0,0 +1,4 @@
+$schema: https://azuremlschemas.azureedge.net/latest/batchEndpoint.schema.json
+name: taxi-fare-batch
+description: taxi cost batch endpoint
+auth_mode: aad_token

classical/rai-aml-cli-v2/mlops/azureml/deploy/online/online-deployment.yml

@@ -0,0 +1,6 @@
+$schema: https://azuremlschemas.azureedge.net/latest/managedOnlineDeployment.schema.json
+name: blue
+endpoint_name: taxi-fare-online
+model: azureml:taxi-model@latest
+instance_type: Standard_DS2_v2
+instance_count: 1

classical/rai-aml-cli-v2/mlops/azureml/deploy/online/online-endpoint.yml

@@ -0,0 +1,4 @@
+$schema: https://azuremlschemas.azureedge.net/latest/managedOnlineEndpoint.schema.json
+name: taxi-fare-online
+description: taxi cost online endpoint
+auth_mode: key

classical/rai-aml-cli-v2/mlops/azureml/train/pipeline.yml

@@ -0,0 +1,166 @@
+$schema: https://azuremlschemas.azureedge.net/latest/pipelineJob.schema.json
+type: pipeline
+experiment_name: taxi-fare-training
+description: Training Pipeline to train a model that predicts taxi fare price
+
+# <inputs_and_outputs>
+inputs:
+  input: #using local data, will create an anonymous data asset
+    type: uri_folder
+    path: ../../../data/
+  enable_monitoring: "true"
+  table_name: 'taximonitoring'
+
+outputs: 
+  train_data:
+  val_data:
+  test_data:
+  trained_model:
+  evaluation_output:
+  model_info_output_path:
+# </inputs_and_outputs>
+
+# <jobs>
+settings:
+  default_datastore: azureml:workspaceblobstore
+  default_compute: azureml:cpu-cluster
+  continue_on_step_failure: false
+
+jobs:
+  prep_data:
+    name: prep_data
+    display_name: prep-data
+    code: ../../../data-science/src/prep
+    command: >-
+      python prep.py 
+      --raw_data ${{inputs.raw_data}} 
+      --train_data ${{outputs.train_data}}
+      --val_data ${{outputs.val_data}}
+      --test_data ${{outputs.test_data}}
+      --enable_monitoring ${{inputs.enable_monitoring}}
+      --table_name ${{inputs.table_name}}
+    environment: azureml:taxi-train-env@latest
+    inputs:
+      raw_data: ${{parent.inputs.input}}
+      enable_monitoring: ${{parent.inputs.enable_monitoring}}
+      table_name: ${{parent.inputs.table_name}}
+    outputs:
+      train_data: ${{parent.outputs.train_data}}
+      val_data: ${{parent.outputs.val_data}}
+      test_data: ${{parent.outputs.test_data}}
+
+  train_model:
+    name: train_model
+    display_name: train-model
+    code: ../../../data-science/src/train
+    command: >-
+      python train.py 
+      --train_data ${{inputs.train_data}} 
+      --model_output ${{outputs.model_output}}
+    environment: azureml:taxi-train-env@latest
+    inputs:
+      train_data: ${{parent.jobs.prep_data.outputs.train_data}}
+    outputs:
+      model_output: ${{parent.outputs.trained_model}}
+  
+  evaluate_model:
+    name: evaluate_model
+    display_name: evaluate-model
+    code: ../../../data-science/src/evaluate
+    command: >-
+      python evaluate.py 
+      --model_name ${{inputs.model_name}} 
+      --model_input ${{inputs.model_input}} 
+      --test_data ${{inputs.test_data}} 
+      --evaluation_output ${{outputs.evaluation_output}}
+    environment: azureml:taxi-train-env@latest
+    inputs:
+      model_name: "taxi-model"
+      model_input: ${{parent.jobs.train_model.outputs.model_output}}
+      test_data: ${{parent.jobs.prep_data.outputs.test_data}}
+    outputs:
+      evaluation_output: ${{parent.outputs.evaluation_output}}
+
+  register_model:
+    name: register_model
+    display_name: register-model
+    code: ../../../data-science/src/register
+    command: >-
+      python register.py 
+      --model_name ${{inputs.model_name}} 
+      --model_path ${{inputs.model_path}} 
+      --evaluation_output ${{inputs.evaluation_output}}
+      --model_info_output_path ${{outputs.model_info_output_path}}
+    environment: azureml:taxi-train-env@latest
+    inputs:
+      model_name: "taxi-model"
+      model_path: ${{parent.jobs.train_model.outputs.model_output}}
+      evaluation_output: ${{parent.jobs.evaluate_model.outputs.evaluation_output}}
+    outputs:
+      model_info_output_path: ${{parent.outputs.model_info_output_path}}
+
+  create_rai_job:
+    type: command
+    component: azureml:rai_insights_constructor@latest
+    limits:
+      timeout: 120
+    inputs:
+      title: Responsible AI for Taxi Fare Prediction
+      task_type: regression
+      model_info_path: ${{parent.jobs.register_model.outputs.model_info_output_path}}
+      train_dataset: ${{parent.jobs.prep_data.outputs.train_data}}
+      test_dataset: ${{parent.jobs.prep_data.outputs.test_data}}
+      target_column_name: "cost"
+      categorical_column_names: '["store_forward", "vendor"]'
+
+  explain_01:
+    type: command
+    component: azureml:rai_insights_explanation@latest
+    limits:
+      timeout: 120
+    inputs:
+      comment: Some random string
+      rai_insights_dashboard: ${{parent.jobs.create_rai_job.outputs.rai_insights_dashboard}}
+
+  causal_01:
+    type: command
+    component: azureml:rai_insights_causal@latest
+    limits:
+      timeout: 120
+    inputs:
+      rai_insights_dashboard: ${{parent.jobs.create_rai_job.outputs.rai_insights_dashboard}}
+      treatment_features: '["distance", "passengers"]'
+      heterogeneity_features: '["store_forward", "vendor"]'
+
+  counterfactual_01:
+    type: command
+    component: azureml:rai_insights_counterfactual@latest
+    limits:
+      timeout: 600
+    inputs:
+      rai_insights_dashboard: ${{parent.jobs.create_rai_job.outputs.rai_insights_dashboard}}
+      total_CFs: 10
+      desired_range: '[16, 30]'
+      feature_importance: True
+
+  error_analysis_01:
+    type: command
+    component: azureml:rai_insights_erroranalysis@latest
+    limits:
+      timeout: 120
+    inputs:
+      rai_insights_dashboard: ${{parent.jobs.create_rai_job.outputs.rai_insights_dashboard}}
+      filter_features: '["distance", "passengers"]'
+
+  gather_01:
+    type: command
+    component: azureml:rai_insights_gather@latest
+    limits:
+      timeout: 120
+    inputs:
+      constructor: ${{parent.jobs.create_rai_job.outputs.rai_insights_dashboard}}
+      insight_1: ${{parent.jobs.causal_01.outputs.causal}}
+      insight_2: ${{parent.jobs.counterfactual_01.outputs.counterfactual}}
+      insight_3: ${{parent.jobs.error_analysis_01.outputs.error_analysis}}
+      insight_4: ${{parent.jobs.explain_01.outputs.explanation}}
+# </jobs>

classical/rai-aml-cli-v2/mlops/azureml/train/train-env.yml

@@ -0,0 +1,5 @@
+$schema: https://azuremlschemas.azureedge.net/latest/environment.schema.json
+name: taxi-train-env
+image: mcr.microsoft.com/azureml/openmpi3.1.2-ubuntu18.04
+conda_file: ../../../data-science/environment/train-conda.yml
+description: Environment created from a Docker image plus Conda environment to train taxi model.

classical/rai-aml-cli-v2/mlops/devops-pipelines/deploy-batch-endpoint-pipeline.yml

@@ -0,0 +1,66 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+variables:
+- ${{ if eq(variables['Build.SourceBranchName'], 'main') }}:
+    # 'main' branch: PRD environment
+    - template: ../../config-infra-prod.yml
+- ${{ if ne(variables['Build.SourceBranchName'], 'main') }}:  
+    # 'develop' or feature branches: DEV environment
+    - template: ../../config-infra-dev.yml
+- name: version
+  value: aml-cli-v2 
+- name: endpoint_name
+  value: taxi-batch-$(namespace)$(postfix)$(environment)
+- name: endpoint_type
+  value: batch
+
+trigger:
+- none
+
+pool:
+  vmImage: ubuntu-20.04
+
+
+resources:
+  repositories:
+    - repository: mlops-templates  # Template Repo
+      name: Azure/mlops-templates # need to change org name from "Azure" to your own org
+      endpoint: github-connection # need to set up and hardcode
+      type: github
+      ref: main
+      
+
+stages:
+- stage: CreateBatchEndpoint
+  displayName: Create/Update Batch Endpoint 
+  jobs:
+    - job: DeployBatchEndpoint
+      steps:
+      - checkout: self
+        path: s/
+      - checkout: mlops-templates
+        path: s/templates/
+      - template: templates/${{ variables.version }}/install-az-cli.yml@mlops-templates
+      - template: templates/${{ variables.version }}/install-aml-cli.yml@mlops-templates
+      - template: templates/${{ variables.version }}/connect-to-workspace.yml@mlops-templates
+      - template: templates/${{ variables.version }}/create-compute.yml@mlops-templates
+        parameters:
+          cluster_name: batch-cluster # name must match cluster name in deployment file below
+          size: STANDARD_DS3_V2
+          min_instances: 0
+          max_instances: 5
+          cluster_tier: dedicated
+      - template: templates/${{ variables.version }}/create-endpoint.yml@mlops-templates
+        parameters: 
+          endpoint_file: mlops/azureml/deploy/batch/batch-endpoint.yml
+      - template: templates/${{ variables.version }}/create-deployment.yml@mlops-templates
+        parameters:
+          deployment_name: taxi-batch-dp
+          deployment_file: mlops/azureml/deploy/batch/batch-deployment.yml      
+      - template: templates/${{ variables.version }}/test-deployment.yml@mlops-templates
+        parameters:
+          deployment_name: taxi-batch-dp
+          sample_request: data/taxi-batch.csv
+          request_type: uri_file #either uri_folder or uri_file 
+

classical/rai-aml-cli-v2/mlops/devops-pipelines/deploy-model-training-pipeline.yml

@@ -0,0 +1,68 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+variables:
+- ${{ if eq(variables['Build.SourceBranchName'], 'main') }}:
+    # 'main' branch: PRD environment
+    - template: ../../config-infra-prod.yml
+- ${{ if ne(variables['Build.SourceBranchName'], 'main') }}:  
+    # 'develop' or feature branches: DEV environment
+    - template: ../../config-infra-dev.yml
+- name: version
+  value: aml-cli-v2
+
+
+trigger:
+- none
+
+pool:
+  vmImage: ubuntu-20.04
+
+
+resources:
+  repositories:
+    - repository: mlops-templates  # Template Repo
+      name: Azure/mlops-templates # need to change org name from "Azure" to your own org
+      endpoint: github-connection # need to set up and hardcode
+      type: github
+      ref: main
+    - repository: rai-vnext-preview  # Template Repo
+      name: Azure/rai-vnext-preview # need to change org name from "Azure" to your own org
+      endpoint: github-connection # need to set up and hardcode
+      type: github
+      ref: main
+
+stages:
+- stage: DeployTrainingPipeline
+  displayName: Deploy Training Pipeline
+  jobs:
+    - job: DeployTrainingPipeline
+      timeoutInMinutes: 120 # how long to run the job before automatically cancelling
+      steps:
+      - checkout: self
+        path: s/
+      - checkout: mlops-templates
+        path: s/templates/
+      - template: templates/tests/unit-tests.yml@mlops-templates
+      - template: templates/${{ variables.version }}/install-az-cli.yml@mlops-templates
+      - template: templates/${{ variables.version }}/install-aml-cli.yml@mlops-templates
+      - template: templates/${{ variables.version }}/connect-to-workspace.yml@mlops-templates
+      - template: templates/${{ variables.version }}/register-environment.yml@mlops-templates
+        parameters:
+          build_type: conda
+          environment_name: taxi-train-env
+          environment_file: mlops/azureml/train/train-env.yml
+          enable_monitoring: $(enable_monitoring)
+      - checkout: rai-vnext-preview
+        path: s/
+      - template: register-rai-components.yml
+      - checkout: self
+        path: s/
+      - checkout: mlops-templates
+        path: s/templates/
+      - template: templates/${{ variables.version }}/run-pipeline.yml@mlops-templates
+        parameters: 
+          pipeline_file: mlops/azureml/train/pipeline.yml
+          experiment_name: $(environment)_taxi_fare_train_$(Build.SourceBranchName)
+          display_name: $(environment)_taxi_fare_run_$(Build.BuildID)
+          enable_monitoring: $(enable_monitoring)

classical/rai-aml-cli-v2/mlops/devops-pipelines/deploy-online-endpoint-pipeline.yml

@@ -0,0 +1,61 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+variables:
+- ${{ if eq(variables['Build.SourceBranchName'], 'main') }}:
+    # 'main' branch: PRD environment
+    - template: ../../config-infra-prod.yml
+- ${{ if ne(variables['Build.SourceBranchName'], 'main') }}:  
+    # 'develop' or feature branches: DEV environment
+    - template: ../../config-infra-dev.yml
+- name: version
+  value: aml-cli-v2 
+- name: endpoint_name
+  value: taxi-online-$(namespace)$(postfix)$(environment)
+- name: endpoint_type
+  value: online
+
+
+trigger:
+- none
+
+pool:
+  vmImage: ubuntu-20.04
+
+
+resources:
+  repositories:
+    - repository: mlops-templates  # Template Repo
+      name: Azure/mlops-templates # need to change org name from "Azure" to your own org
+      endpoint: github-connection # need to set up and hardcode
+      type: github
+      ref: main
+
+stages:
+- stage: CreateOnlineEndpoint
+  displayName: Create/Update Online Endpoint 
+  jobs:
+    - job: DeployOnlineEndpoint
+      steps:
+      - checkout: self
+        path: s/
+      - checkout: mlops-templates
+        path: s/templates/
+      - template: templates/${{ variables.version }}/install-az-cli.yml@mlops-templates
+      - template: templates/${{ variables.version }}/install-aml-cli.yml@mlops-templates
+      - template: templates/${{ variables.version }}/connect-to-workspace.yml@mlops-templates
+      - template: templates/${{ variables.version }}/create-endpoint.yml@mlops-templates
+        parameters:
+          endpoint_file: mlops/azureml/deploy/online/online-endpoint.yml
+      - template: templates/${{ variables.version }}/create-deployment.yml@mlops-templates
+        parameters:
+          deployment_name: taxi-online-dp
+          deployment_file: mlops/azureml/deploy/online/online-deployment.yml 
+      - template: templates/${{ variables.version }}/allocate-traffic.yml@mlops-templates
+        parameters:
+          traffic_allocation: taxi-online-dp=100
+      - template: templates/${{ variables.version }}/test-deployment.yml@mlops-templates
+        parameters:
+          deployment_name: taxi-online-dp
+          sample_request: data/taxi-request.json
+          request_type: json