diff --git a/.env.example b/.env.example
index 3c63d969..36eb0c54 100644
--- a/.env.example
+++ b/.env.example
@@ -69,8 +69,8 @@ AML_CLUSTER_MAX_NODES_SCORING = '4'
 AML_CLUSTER_MIN_NODES_SCORING = '0'
 AML_CLUSTER_PRIORITY_SCORING = 'lowpriority'
 AML_REBUILD_ENVIRONMENT_SCORING = 'true'
-BATCHSCORE_SCRIPT_PATH = 'scoring/parallel_batchscore.py'
-BATCHSCORE_COPY_SCRIPT_PATH = 'scoring/parallel_batchscore_copyoutput.py'
+BATCHSCORE_SCRIPT_PATH = 'diabetes_regression/scoring/parallel_batchscore.py'
+BATCHSCORE_COPY_SCRIPT_PATH = 'diabetes_regression/scoring/parallel_batchscore_copyoutput.py'
 
 
 SCORING_DATASTORE_INPUT_CONTAINER = 'input'
@@ -78,4 +78,7 @@ SCORING_DATASTORE_INPUT_FILENAME = 'diabetes_scoring_input.csv'
 SCORING_DATASTORE_OUTPUT_CONTAINER = 'output'
 SCORING_DATASTORE_OUTPUT_FILENAME = 'diabetes_scoring_output.csv'
 SCORING_DATASET_NAME = 'diabetes_scoring_ds'
-SCORING_PIPELINE_NAME = 'diabetes-scoring-pipeline'
\ No newline at end of file
+SCORING_PIPELINE_NAME = 'diabetes-scoring-pipeline'
+
+# Observability
+APP_INSIGHTS_CONNECTION_STRING = ''
\ No newline at end of file
diff --git a/.gitignore b/.gitignore
index 3ab04e2f..6902cfd3 100644
--- a/.gitignore
+++ b/.gitignore
@@ -109,3 +109,6 @@ condaenv.*
 .mypy_cache/
 
 .DS_Store
+
+#pycharm
+.idea
\ No newline at end of file
diff --git a/.pipelines/diabetes_regression-batchscoring-ci.yml b/.pipelines/diabetes_regression-batchscoring-ci.yml
index 1392fddb..7ee42d17 100644
--- a/.pipelines/diabetes_regression-batchscoring-ci.yml
+++ b/.pipelines/diabetes_regression-batchscoring-ci.yml
@@ -68,6 +68,8 @@ stages:
           python -m ml_service.pipelines.diabetes_regression_build_parallel_batchscore_pipeline
       env:
         SCORING_DATASTORE_ACCESS_KEY: $(SCORING_DATASTORE_ACCESS_KEY)
+        APP_INSIGHTS_CONNECTION_STRING: $(APP_INSIGHTS_CONNECTION_STRING)
+
 
   - job: "Run_Batch_Score_Pipeline"
     displayName: "Run Batch Scoring Pipeline"
diff --git a/.pipelines/diabetes_regression-cd.yml b/.pipelines/diabetes_regression-cd.yml
index 8dd35e47..7e43098c 100644
--- a/.pipelines/diabetes_regression-cd.yml
+++ b/.pipelines/diabetes_regression-cd.yml
@@ -55,7 +55,7 @@ stages:
       inputs:
         azureSubscription: '$(WORKSPACE_SVC_CONNECTION)'
         scriptLocation: inlineScript
-        workingDirectory: $(Build.SourcesDirectory)/$(SOURCES_DIR_TRAIN)/scoring
+        workingDirectory: $(Build.SourcesDirectory)/$(SOURCES_DIR_TRAIN)/diabetes_regression/scoring
         inlineScript: |
           set -e # fail on error
           
@@ -101,7 +101,7 @@ stages:
       inputs:
         azureSubscription: '$(WORKSPACE_SVC_CONNECTION)'
         scriptLocation: inlineScript
-        workingDirectory: $(Build.SourcesDirectory)/$(SOURCES_DIR_TRAIN)/scoring
+        workingDirectory: $(Build.SourcesDirectory)/$(SOURCES_DIR_TRAIN)/diabetes_regression/scoring
         inlineScript: |
           set -e # fail on error
           
@@ -111,6 +111,8 @@ stages:
           --dc deployment_config_aks.yml \
           -g $(RESOURCE_GROUP) --workspace-name $(WORKSPACE_NAME) \
           --overwrite -v
+      env:
+        APP_INSIGHTS_CONNECTION_STRING: $(APP_INSIGHTS_CONNECTION_STRING)
     - task: AzureCLI@1
       displayName: 'Smoke test'
       inputs:
@@ -120,6 +122,8 @@ stages:
           set -e # fail on error
           export SUBSCRIPTION_ID=$(az account show --query id -o tsv)
           python -m ml_service.util.smoke_test_scoring_service --type AKS --service "$(AKS_DEPLOYMENT_NAME)"
+      env:
+        APP_INSIGHTS_CONNECTION_STRING: $(APP_INSIGHTS_CONNECTION_STRING)
 
 - stage: 'Deploy_Webapp'
   displayName: 'Deploy to Webapp'
@@ -138,8 +142,8 @@ stages:
     - template: diabetes_regression-package-model-template.yml
       parameters:
         modelId: $(MODEL_NAME):$(get_model.MODEL_VERSION)
-        scoringScriptPath: '$(Build.SourcesDirectory)/$(SOURCES_DIR_TRAIN)/scoring/score.py'
-        condaFilePath: '$(Build.SourcesDirectory)/$(SOURCES_DIR_TRAIN)/conda_dependencies.yml'
+        scoringScriptPath: '$(Build.SourcesDirectory)/$(SOURCES_DIR_TRAIN)/diabetes_regression/scoring/score.py'
+        condaFilePath: '$(Build.SourcesDirectory)/$(SOURCES_DIR_TRAIN)/diabetes_regression/conda_dependencies.yml'
     - script: echo $(IMAGE_LOCATION) >image_location.txt
       displayName: "Write image location file"
     - task: AzureWebAppContainer@1
@@ -159,3 +163,5 @@ stages:
           set -e # fail on error
           export SUBSCRIPTION_ID=$(az account show --query id -o tsv)
           python -m ml_service.util.smoke_test_scoring_service --type Webapp --service "$(WebAppDeploy.AppServiceApplicationUrl)/score"
+      env:
+        APP_INSIGHTS_CONNECTION_STRING: $(APP_INSIGHTS_CONNECTION_STRING)
diff --git a/.pipelines/diabetes_regression-ci.yml b/.pipelines/diabetes_regression-ci.yml
index 5a539af0..f7e30e94 100644
--- a/.pipelines/diabetes_regression-ci.yml
+++ b/.pipelines/diabetes_regression-ci.yml
@@ -45,6 +45,8 @@ stages:
           # Invoke the Python building and publishing a training pipeline
           python -m ml_service.pipelines.diabetes_regression_build_train_pipeline
       displayName: 'Publish Azure Machine Learning Pipeline'
+      env:
+        APP_INSIGHTS_CONNECTION_STRING: $(APP_INSIGHTS_CONNECTION_STRING)
 
 - stage: 'Trigger_AML_Pipeline'
   displayName: 'Train and evaluate model'
@@ -70,6 +72,8 @@ stages:
           # Set AMLPIPELINEID variable for next AML Pipeline task in next job
           AMLPIPELINEID="$(cat pipeline_id.txt)"
           echo "##vso[task.setvariable variable=AMLPIPELINEID;isOutput=true]$AMLPIPELINEID"
+      env:
+        APP_INSIGHTS_CONNECTION_STRING: $(APP_INSIGHTS_CONNECTION_STRING)
       name: 'getpipelineid'
       displayName: 'Get Pipeline ID'
   - job: "Run_ML_Pipeline"
@@ -87,6 +91,8 @@ stages:
         PipelineId: '$(AMLPIPELINE_ID)'
         ExperimentName: '$(EXPERIMENT_NAME)'
         PipelineParameters: '"ParameterAssignments": {"model_name": "$(MODEL_NAME)"}, "tags": {"BuildId": "$(Build.BuildId)", "BuildUri": "$(BUILD_URI)"}, "StepTags": {"BuildId": "$(Build.BuildId)", "BuildUri": "$(BUILD_URI)"}'
+      env:
+        APP_INSIGHTS_CONNECTION_STRING: $(APP_INSIGHTS_CONNECTION_STRING)
   - job: "Training_Run_Report"
     dependsOn: "Run_ML_Pipeline"
     condition: always()
diff --git a/.pipelines/diabetes_regression-get-model-id-artifact-template.yml b/.pipelines/diabetes_regression-get-model-id-artifact-template.yml
index b9e61306..d1fc8a2c 100644
--- a/.pipelines/diabetes_regression-get-model-id-artifact-template.yml
+++ b/.pipelines/diabetes_regression-get-model-id-artifact-template.yml
@@ -27,6 +27,8 @@ steps:
         runId: '${{ parameters.artifactBuildId }}'
       runBranch: '$(Build.SourceBranch)'
       path: $(Build.SourcesDirectory)/bin
+    env:
+      APP_INSIGHTS_CONNECTION_STRING: $(APP_INSIGHTS_CONNECTION_STRING)
   - task: Bash@3
     name: get_model
     displayName: Parse Json for Model Name and Version
diff --git a/.pipelines/diabetes_regression-publish-model-artifact-template.yml b/.pipelines/diabetes_regression-publish-model-artifact-template.yml
index 00e45105..f796dfce 100644
--- a/.pipelines/diabetes_regression-publish-model-artifact-template.yml
+++ b/.pipelines/diabetes_regression-publish-model-artifact-template.yml
@@ -25,5 +25,7 @@ steps:
       echo $FOUND_MODEL >model.json
   name: 'getversion'
   displayName: "Determine if evaluation succeeded and new model is registered (CLI)"
+  env:
+    APP_INSIGHTS_CONNECTION_STRING: $(APP_INSIGHTS_CONNECTION_STRING)
 - publish: model.json
   artifact: model
diff --git a/.pipelines/diabetes_regression-variables-template.yml b/.pipelines/diabetes_regression-variables-template.yml
index 502753fb..2f5857e6 100644
--- a/.pipelines/diabetes_regression-variables-template.yml
+++ b/.pipelines/diabetes_regression-variables-template.yml
@@ -3,19 +3,19 @@ variables:
   # Source Config
   # The directory containing the scripts for training, evaluating, and registering the model
   - name: SOURCES_DIR_TRAIN
-    value: diabetes_regression
+    value: .
     # The path to the model training script under SOURCES_DIR_TRAIN
   - name: TRAIN_SCRIPT_PATH
-    value: training/train_aml.py
+    value: diabetes_regression/training/train_aml.py
     # The path to the model evaluation script under SOURCES_DIR_TRAIN
   - name: EVALUATE_SCRIPT_PATH
-    value: evaluate/evaluate_model.py
+    value: diabetes_regression/evaluate/evaluate_model.py
     # The path to the model registration script under SOURCES_DIR_TRAIN
   - name: REGISTER_SCRIPT_PATH
-    value: register/register_model.py
+    value: diabetes_regression/register/register_model.py
     # The path to the model scoring script relative to SOURCES_DIR_TRAIN
   - name: SCORE_SCRIPT
-    value: scoring/score.py
+    value: diabetes_regression/scoring/score.py
     
 
   # Azure ML Variables
@@ -66,8 +66,8 @@ variables:
   #   value: "true"
 
   # Flag to allow rebuilding the AML Environment after it was built for the first time. This enables dependency updates from conda_dependencies.yaml.
-  # - name: AML_REBUILD_ENVIRONMENT
-  #  value: "false"
+#  - name: AML_REBUILD_ENVIRONMENT
+#    value: "true"
 
   # Variables below are used for controlling various aspects of batch scoring
   - name: USE_GPU_FOR_SCORING
@@ -95,9 +95,9 @@ variables:
     value: lowpriority
   # The path to the batch scoring script relative to SOURCES_DIR_TRAIN
   - name: BATCHSCORE_SCRIPT_PATH
-    value: scoring/parallel_batchscore.py
+    value: diabetes_regression/scoring/parallel_batchscore.py
   - name: BATCHSCORE_COPY_SCRIPT_PATH
-    value: scoring/parallel_batchscore_copyoutput.py
+    value: diabetes_regression/scoring/parallel_batchscore_copyoutput.py
   # Flag to allow rebuilding the AML Environment after it was built for the first time. 
   # This enables dependency updates from the conda dependencies yaml for scoring activities.
   - name: AML_REBUILD_ENVIRONMENT_SCORING
@@ -126,4 +126,7 @@ variables:
   # Scoring pipeline name
   - name: SCORING_PIPELINE_NAME
     value: "diabetes-scoring-pipeline"
-    
\ No newline at end of file
+
+  #Observability
+  - name: APP_INSIGHTS_CONNECTION_STRING
+    value: ""
diff --git a/diabetes_regression/ci_dependencies.yml b/diabetes_regression/ci_dependencies.yml
index c54f3e32..d77fcd83 100644
--- a/diabetes_regression/ci_dependencies.yml
+++ b/diabetes_regression/ci_dependencies.yml
@@ -27,3 +27,6 @@ dependencies:
       - flake8==3.7.*
       - flake8_formatter_junit_xml==0.0.*
       - azure-cli==2.3.*
+      - opencensus==0.7.7
+      - opencensus-context==0.1.1
+      - opencensus-ext-azure==1.0.2
diff --git a/diabetes_regression/conda_dependencies.yml b/diabetes_regression/conda_dependencies.yml
index 57f2b999..e420fd94 100644
--- a/diabetes_regression/conda_dependencies.yml
+++ b/diabetes_regression/conda_dependencies.yml
@@ -37,3 +37,12 @@ dependencies:
 
       # MLOps with R
       - azure-storage-blob
+
+      # Observability
+      - opencensus==0.7.7
+      - opencensus-context==0.1.1
+      - opencensus-ext-azure==1.0.2
+      - python-dotenv==0.10.3
+
+      # Data Classes
+      - dataclasses
diff --git a/diabetes_regression/conda_dependencies_scorecopy.yml b/diabetes_regression/conda_dependencies_scorecopy.yml
index dffafd08..876bf278 100644
--- a/diabetes_regression/conda_dependencies_scorecopy.yml
+++ b/diabetes_regression/conda_dependencies_scorecopy.yml
@@ -29,3 +29,12 @@ dependencies:
       
       # Score copying deps
       - azure-storage-blob
+
+      # Observability
+      - opencensus==0.7.7
+      - opencensus-context==0.1.1
+      - opencensus-ext-azure==1.0.2
+      - python-dotenv==0.10.3
+
+      # Data Classes
+      - dataclasses
diff --git a/diabetes_regression/conda_dependencies_scoring.yml b/diabetes_regression/conda_dependencies_scoring.yml
index 60c45c44..fca0405e 100644
--- a/diabetes_regression/conda_dependencies_scoring.yml
+++ b/diabetes_regression/conda_dependencies_scoring.yml
@@ -30,3 +30,12 @@ dependencies:
       # Scoring deps
       - scikit-learn
       - pandas
+
+      # Observability
+      - opencensus==0.7.7
+      - opencensus-context==0.1.1
+      - opencensus-ext-azure==1.0.2
+      - python-dotenv==0.10.3
+
+      # Data Classes
+      - dataclasses
diff --git a/diabetes_regression/evaluate/evaluate_model.py b/diabetes_regression/evaluate/evaluate_model.py
index 5a69addb..b7bfab65 100644
--- a/diabetes_regression/evaluate/evaluate_model.py
+++ b/diabetes_regression/evaluate/evaluate_model.py
@@ -26,7 +26,11 @@
 from azureml.core import Run
 import argparse
 import traceback
-from util.model_helper import get_model
+from diabetes_regression.util.model_helper import get_model
+from utils.logger.logger_interface import Severity
+from utils.logger.observability import Observability
+
+observability = Observability()
 
 run = Run.get_context()
 
@@ -42,7 +46,7 @@
 #     load_dotenv()
 #     sources_dir = os.environ.get("SOURCES_DIR_TRAIN")
 #     if (sources_dir is None):
-#         sources_dir = 'diabetes_regression'
+#         sources_dir = '.'
 #     path_to_util = os.path.join(".", sources_dir, "util")
 #     sys.path.append(os.path.abspath(path_to_util))  # NOQA: E402
 #     from model_helper import get_model
@@ -89,7 +93,8 @@
 parser.add_argument(
     "--allow_run_cancel",
     type=str,
-    help="Set this to false to avoid evaluation step from cancelling run after an unsuccessful evaluation",  # NOQA: E501
+    help="Set this to false to avoid evaluation step from cancelling "
+         "run after an unsuccessful evaluation",
     default="true",
 )
 
@@ -109,10 +114,10 @@
     tag_name = 'experiment_name'
 
     model = get_model(
-                model_name=model_name,
-                tag_name=tag_name,
-                tag_value=exp.name,
-                aml_workspace=ws)
+        model_name=model_name,
+        tag_name=tag_name,
+        tag_value=exp.name,
+        aml_workspace=ws)
 
     if (model is not None):
         production_model_mse = 10000
@@ -120,12 +125,12 @@
             production_model_mse = float(model.tags[metric_eval])
         new_model_mse = float(run.parent.get_metrics().get(metric_eval))
         if (production_model_mse is None or new_model_mse is None):
-            print("Unable to find", metric_eval, "metrics, "
-                  "exiting evaluation")
-            if((allow_run_cancel).lower() == 'true'):
+            observability.log("Unable to find" +
+                              metric_eval + "metrics, exiting evaluation")
+            if ((allow_run_cancel).lower() == 'true'):
                 run.parent.cancel()
         else:
-            print(
+            observability.log(
                 "Current Production model mse: {}, "
                 "New trained model mse: {}".format(
                     production_model_mse, new_model_mse
@@ -133,18 +138,23 @@
             )
 
         if (new_model_mse < production_model_mse):
-            print("New trained model performs better, "
-                  "thus it should be registered")
+            observability.log("New trained model performs better, "
+                              "thus it should be registered")
         else:
-            print("New trained model metric is worse than or equal to "
-                  "production model so skipping model registration.")
-            if((allow_run_cancel).lower() == 'true'):
+            observability.log("New trained model metric is worse "
+                              "than or equal to "
+                              "production model so skipping "
+                              "model registration.")
+            if ((allow_run_cancel).lower() == 'true'):
                 run.parent.cancel()
     else:
-        print("This is the first model, "
-              "thus it should be registered")
+        observability.log("This is the first model, "
+                          "thus it should be registered")
 
 except Exception:
     traceback.print_exc(limit=None, file=None, chain=True)
-    print("Something went wrong trying to evaluate. Exiting.")
+    observability.log(
+        description="Something went wrong trying to evaluate. Exiting.",
+        severity=Severity.ERROR)
+
     raise
diff --git a/diabetes_regression/register/register_model.py b/diabetes_regression/register/register_model.py
index bca55a83..452ee896 100644
--- a/diabetes_regression/register/register_model.py
+++ b/diabetes_regression/register/register_model.py
@@ -31,10 +31,13 @@
 import joblib
 from azureml.core import Run, Experiment, Workspace, Dataset
 from azureml.core.model import Model as AMLModel
+from utils.logger.logger_interface import Severity
+from utils.logger.observability import Observability
 
+observability = Observability()
 
-def main():
 
+def main():
     run = Run.get_context()
     if (run.id.startswith('OfflineRun')):
         from dotenv import load_dotenv
@@ -87,15 +90,17 @@ def main():
     model_name = args.model_name
     model_path = args.step_input
 
-    print("Getting registration parameters")
+    observability.log("Getting registration parameters")
 
     # Load the registration parameters from the parameters file
-    with open("parameters.json") as f:
+    with open("diabetes_regression/parameters.json") as f:
         pars = json.load(f)
     try:
         register_args = pars["registration"]
     except KeyError:
-        print("Could not load registration values from file")
+        observability.log(
+            description="Could not load registration values from file",
+            severity=Severity.ERROR)
         register_args = {"tags": []}
 
     model_tags = {}
@@ -104,10 +109,12 @@ def main():
             mtag = run.parent.get_metrics()[tag]
             model_tags[tag] = mtag
         except KeyError:
-            print(f"Could not find {tag} metric on parent run.")
+            observability.log(
+                description=f"Could not find {tag} metric on parent run.",
+                severity=Severity.ERROR)
 
     # load the model
-    print("Loading model from " + model_path)
+    observability.log("Loading model from " + model_path)
     model_file = os.path.join(model_path, model_name)
     model = joblib.load(model_file)
     parent_tags = run.parent.get_tags()
@@ -115,14 +122,18 @@ def main():
         build_id = parent_tags["BuildId"]
     except KeyError:
         build_id = None
-        print("BuildId tag not found on parent run.")
-        print(f"Tags present: {parent_tags}")
+        observability.log("BuildId tag not found on parent run.",
+                          severity=Severity.ERROR)
+        observability.log(description=f"Tags present: {parent_tags}",
+                          severity=Severity.ERROR)
     try:
         build_uri = parent_tags["BuildUri"]
     except KeyError:
         build_uri = None
-        print("BuildUri tag not found on parent run.")
-        print(f"Tags present: {parent_tags}")
+        observability.log(description="BuildUri tag not found on parent run.",
+                          severity=Severity.ERROR)
+        observability.log(description=f"Tags present: {parent_tags}",
+                          severity=Severity.ERROR)
 
     if (model is not None):
         dataset_id = parent_tags["dataset_id"]
@@ -154,7 +165,7 @@ def main():
                 build_id,
                 build_uri)
     else:
-        print("Model not found. Skipping model registration.")
+        observability.log("Model not found. Skipping model registration.")
         sys.exit(0)
 
 
@@ -163,21 +174,21 @@ def model_already_registered(model_name, exp, run_id):
     if len(model_list) >= 1:
         e = ("Model name:", model_name, "in workspace",
              exp.workspace, "with run_id ", run_id, "is already registered.")
-        print(e)
+        observability.log(description=e, severity=Severity.ERROR)
         raise Exception(e)
     else:
-        print("Model is not registered for this run.")
+        observability.log("Model is not registered for this run.")
 
 
 def register_aml_model(
-    model_path,
-    model_name,
-    model_tags,
-    exp,
-    run_id,
-    dataset_id,
-    build_id: str = 'none',
-    build_uri=None
+        model_path,
+        model_name,
+        model_tags,
+        exp,
+        run_id,
+        dataset_id,
+        build_id: str = 'none',
+        build_uri=None
 ):
     try:
         tagsValue = {"area": "diabetes_regression",
@@ -198,7 +209,7 @@ def register_aml_model(
             datasets=[('training data',
                        Dataset.get_by_id(exp.workspace, dataset_id))])
         os.chdir("..")
-        print(
+        observability.log(
             "Model registered: {} \nModel Description: {} "
             "\nModel Version: {}".format(
                 model.name, model.description, model.version
@@ -206,7 +217,8 @@ def register_aml_model(
         )
     except Exception:
         traceback.print_exc(limit=None, file=None, chain=True)
-        print("Model registration failed")
+        observability.log("Model registration failed",
+                          severity=Severity.ERROR)
         raise
 
 
diff --git a/diabetes_regression/scoring/parallel_batchscore.py b/diabetes_regression/scoring/parallel_batchscore.py
index cd42c79c..d47a6bfb 100644
--- a/diabetes_regression/scoring/parallel_batchscore.py
+++ b/diabetes_regression/scoring/parallel_batchscore.py
@@ -29,8 +29,12 @@
 import joblib
 import sys
 from typing import List
-from util.model_helper import get_model
+from diabetes_regression.util.model_helper import get_model
 from azureml.core import Model
+from utils.logger.logger_interface import Severity
+from utils.logger.observability import Observability
+
+observability = Observability()
 
 model = None
 
@@ -68,7 +72,7 @@ def parse_args() -> List[str]:
     model_version = (
         None
         if len(model_version_param) < 1
-        or len(model_version_param[0][1].strip()) == 0  # NOQA: E501
+           or len(model_version_param[0][1].strip()) == 0  # NOQA: E501
         else model_version_param[0][1]
     )
 
@@ -80,7 +84,7 @@ def parse_args() -> List[str]:
     model_tag_name = (
         None
         if len(model_tag_name_param) < 1
-        or len(model_tag_name_param[0][1].strip()) == 0  # NOQA: E501
+           or len(model_tag_name_param[0][1].strip()) == 0  # NOQA: E501
         else model_tag_name_param[0][1]
     )
 
@@ -105,7 +109,7 @@ def init():
     line arguments and get the right model to use for scoring.
     """
     try:
-        print("Initializing batch scoring script...")
+        observability.log("Initializing batch scoring script...")
 
         # Get the model using name/version/tags filter
         model_filter = parse_args()
@@ -120,9 +124,10 @@ def init():
         modelpath = Model.get_model_path(
             model_name=amlmodel.name, version=amlmodel.version)
         model = joblib.load(modelpath)
-        print("Loaded model {}".format(model_filter[0]))
+        observability.log("Loaded model {}".format(model_filter[0]))
     except Exception as ex:
-        print("Error: {}".format(ex))
+        observability.log(description="Error: {}".format(ex),
+                          severity=Severity.ERROR)
 
 
 def run(mini_batch: pd.DataFrame) -> pd.DataFrame:
@@ -154,4 +159,4 @@ def run(mini_batch: pd.DataFrame) -> pd.DataFrame:
         )
 
     except Exception as ex:
-        print(ex)
+        observability.log(description=ex, severity=Severity.ERROR)
diff --git a/diabetes_regression/scoring/parallel_batchscore_copyoutput.py b/diabetes_regression/scoring/parallel_batchscore_copyoutput.py
index cc4af42c..64f04bfd 100644
--- a/diabetes_regression/scoring/parallel_batchscore_copyoutput.py
+++ b/diabetes_regression/scoring/parallel_batchscore_copyoutput.py
@@ -28,6 +28,9 @@
 from datetime import datetime, date, timezone
 import argparse
 import os
+from utils.logger.observability import Observability
+
+observability = Observability()
 
 
 def parse_args():
@@ -42,7 +45,7 @@ def parse_args():
 
 
 def copy_output(args):
-    print("Output : {}".format(args.output_path))
+    observability.log("Output : {}".format(args.output_path))
 
     accounturl = "https://{}.blob.core.windows.net".format(
         args.scoring_datastore
@@ -86,6 +89,6 @@ def copy_output(args):
         or args.output_path is None
         or args.output_path.strip() == ""
     ):
-        print("Missing parameters")
+        observability.log("Missing parameters")
     else:
         copy_output(args)
diff --git a/diabetes_regression/scoring/score.py b/diabetes_regression/scoring/score.py
index 4acd5c8d..16e43690 100644
--- a/diabetes_regression/scoring/score.py
+++ b/diabetes_regression/scoring/score.py
@@ -31,6 +31,9 @@
     import input_schema, output_schema
 from inference_schema.parameter_types.numpy_parameter_type \
     import NumpyParameterType
+from utils.logger.observability import Observability
+
+observability = Observability()
 
 
 def init():
@@ -70,13 +73,13 @@ def run(data, request_headers):
     # The HTTP 'traceparent' header may be set by the caller to implement
     # distributed tracing (per the W3C Trace Context proposed specification)
     # and can be used to correlate the request to external systems.
-    print(('{{"RequestId":"{0}", '
-           '"TraceParent":"{1}", '
-           '"NumberOfPredictions":{2}}}'
-           ).format(
-               request_headers.get("X-Ms-Request-Id", ""),
-               request_headers.get("Traceparent", ""),
-               len(result)
+    observability.log(('{{"RequestId":"{0}", '
+                       '"TraceParent":"{1}", '
+                       '"NumberOfPredictions":{2}}}'
+                       ).format(
+        request_headers.get("X-Ms-Request-Id", ""),
+        request_headers.get("Traceparent", ""),
+        len(result)
     ))
 
     return {"result": result.tolist()}
@@ -87,4 +90,4 @@ def run(data, request_headers):
     init()
     test_row = '{"data":[[1,2,3,4,5,6,7,8,9,10],[10,9,8,7,6,5,4,3,2,1]]}'
     prediction = run(test_row, {})
-    print("Test result: ", prediction)
+    observability.log("Test result: ", prediction)
diff --git a/diabetes_regression/training/train.py b/diabetes_regression/training/train.py
index 22258042..03a531dd 100644
--- a/diabetes_regression/training/train.py
+++ b/diabetes_regression/training/train.py
@@ -29,6 +29,9 @@
 from sklearn.linear_model import Ridge
 from sklearn.metrics import mean_squared_error
 from sklearn.model_selection import train_test_split
+from utils.logger.observability import Observability
+
+observability = Observability()
 
 
 # Split the dataframe into test and train data
@@ -59,7 +62,7 @@ def get_model_metrics(model, data):
 
 
 def main():
-    print("Running train.py")
+    observability.log("Running train.py")
 
     # Define training parameters
     ridge_args = {"alpha": 0.5}
@@ -77,7 +80,7 @@ def main():
     # Log the metrics for the model
     metrics = get_model_metrics(model, data)
     for (k, v) in metrics.items():
-        print(f"{k}: {v}")
+        observability.log_metric(name=k, value=v)
 
 
 if __name__ == '__main__':
diff --git a/diabetes_regression/training/train_aml.py b/diabetes_regression/training/train_aml.py
index 9303198b..ab08f49e 100644
--- a/diabetes_regression/training/train_aml.py
+++ b/diabetes_regression/training/train_aml.py
@@ -29,14 +29,19 @@
 import argparse
 import joblib
 import json
-from train import split_data, train_model, get_model_metrics
+from diabetes_regression.training.train import split_data, \
+    train_model, get_model_metrics
+from utils.logger.logger_interface import Severity
+from utils.logger.observability import Observability
+
+observability = Observability()
 
 
 def register_dataset(
-    aml_workspace: Workspace,
-    dataset_name: str,
-    datastore_name: str,
-    file_path: str
+        aml_workspace: Workspace,
+        dataset_name: str,
+        datastore_name: str,
+        file_path: str
 ) -> Dataset:
     datastore = Datastore.get(aml_workspace, datastore_name)
     dataset = Dataset.Tabular.from_delimited_files(path=(datastore, file_path))
@@ -48,7 +53,7 @@ def register_dataset(
 
 
 def main():
-    print("Running train_aml.py")
+    observability.log("Running train_aml.py")
 
     parser = argparse.ArgumentParser("train")
     parser.add_argument(
@@ -93,12 +98,12 @@ def main():
 
     args = parser.parse_args()
 
-    print("Argument [model_name]: %s" % args.model_name)
-    print("Argument [step_output]: %s" % args.step_output)
-    print("Argument [dataset_version]: %s" % args.dataset_version)
-    print("Argument [data_file_path]: %s" % args.data_file_path)
-    print("Argument [caller_run_id]: %s" % args.caller_run_id)
-    print("Argument [dataset_name]: %s" % args.dataset_name)
+    observability.log("Argument [model_name]: %s" % args.model_name)
+    observability.log("Argument [step_output]: %s" % args.step_output)
+    observability.log("Argument [dataset_version]: %s" % args.dataset_version)
+    observability.log("Argument [data_file_path]: %s" % args.data_file_path)
+    observability.log("Argument [caller_run_id]: %s" % args.caller_run_id)
+    observability.log("Argument [dataset_name]: %s" % args.dataset_name)
 
     model_name = args.model_name
     step_output_path = args.step_output
@@ -108,19 +113,21 @@ def main():
 
     run = Run.get_context()
 
-    print("Getting training parameters")
+    observability.log("Getting training parameters")
 
     # Load the training parameters from the parameters file
-    with open("parameters.json") as f:
+    with open("diabetes_regression/parameters.json") as f:
         pars = json.load(f)
     try:
         train_args = pars["training"]
     except KeyError:
-        print("Could not load training values from file")
+        observability.log(
+            description="Could not load training values from file",
+            severity=Severity.ERROR)
         train_args = {}
 
     # Log the training parameters
-    print(f"Parameters: {train_args}")
+    observability.log(f"Parameters: {train_args}")
     for (k, v) in train_args.items():
         run.log(k, v)
         run.parent.log(k, v)
@@ -136,7 +143,7 @@ def main():
                                        data_file_path)
     else:
         e = ("No dataset provided")
-        print(e)
+        observability.log(description=e, severity=Severity.ERROR)
         raise Exception(e)
 
     # Link dataset to the step run so it is trackable in the UI
@@ -153,8 +160,8 @@ def main():
     # Evaluate and log the metrics returned from the train function
     metrics = get_model_metrics(model, data)
     for (k, v) in metrics.items():
-        run.log(k, v)
-        run.parent.log(k, v)
+        observability.log_metric(name=k, value=v)
+        observability.log_metric(name=k, value=v, log_parent=True)
 
     # Pass model file to next step
     os.makedirs(step_output_path, exist_ok=True)
@@ -167,7 +174,7 @@ def main():
     joblib.dump(value=model, filename=output_path)
 
     run.tag("run_type", value="train")
-    print(f"tags now present for run: {run.tags}")
+    observability.log(f"tags now present for run: {run.tags}")
 
     run.complete()
 
diff --git a/diabetes_regression/util/model_helper.py b/diabetes_regression/util/model_helper.py
index 0fd20ef0..d175930a 100644
--- a/diabetes_regression/util/model_helper.py
+++ b/diabetes_regression/util/model_helper.py
@@ -4,6 +4,10 @@
 from azureml.core import Run
 from azureml.core import Workspace
 from azureml.core.model import Model as AMLModel
+from utils.logger.logger_interface import Severity
+from utils.logger.observability import Observability
+
+observability = Observability()
 
 
 def get_current_workspace() -> Workspace:
@@ -44,17 +48,19 @@ def get_model(
     None.
     """
     if aml_workspace is None:
-        print("No workspace defined - using current experiment workspace.")
+        observability.log("No workspace defined - "
+                          "using current experiment workspace.")
         aml_workspace = get_current_workspace()
 
     tags = None
     if tag_name is not None or tag_value is not None:
         # Both a name and value must be specified to use tags.
         if tag_name is None or tag_value is None:
-            raise ValueError(
-                "model_tag_name and model_tag_value should both be supplied"
-                + "or excluded"  # NOQA: E501
-            )
+
+            error = "model_tag_name and model_tag_value should " \
+                     "both be supplied or excluded"
+            observability.log(description=error, severity=Severity.ERROR)
+            raise ValueError(error)
         tags = [[tag_name, tag_value]]
 
     model = None
@@ -74,6 +80,8 @@ def get_model(
         if len(models) == 1:
             model = models[0]
         elif len(models) > 1:
-            raise Exception("Expected only one model")
+            error = "Expected only one model"
+            observability.log(description=error, severity=Severity.ERROR)
+            raise Exception(error)
 
     return model
diff --git a/ml_service/pipelines/diabetes_regression_build_parallel_batchscore_pipeline.py b/ml_service/pipelines/diabetes_regression_build_parallel_batchscore_pipeline.py
index ac3d3407..f72874c1 100644
--- a/ml_service/pipelines/diabetes_regression_build_parallel_batchscore_pipeline.py
+++ b/ml_service/pipelines/diabetes_regression_build_parallel_batchscore_pipeline.py
@@ -41,9 +41,14 @@
 from azureml.pipeline.steps import PythonScriptStep
 from typing import Tuple
 
+from utils.logger.logger_interface import Severity
+from utils.logger.observability import Observability
+
+observability = Observability()
+
 
 def get_or_create_datastore(
-    datastorename: str, ws: Workspace, env: Env, input: bool = True
+        datastorename: str, ws: Workspace, env: Env, input: bool = True
 ) -> Datastore:
     """
     Obtains a datastore with matching name. Creates it if none exists.
@@ -59,7 +64,9 @@ def get_or_create_datastore(
     :raises: ValueError
     """
     if datastorename is None:
-        raise ValueError("Datastore name is required.")
+        error = "Datastore name is required."
+        observability.log(description=error, severity=Severity.ERROR)
+        raise ValueError(error)
 
     containername = (
         env.scoring_datastore_input_container
@@ -73,8 +80,8 @@ def get_or_create_datastore(
 
     # the datastore is not registered but we have all details to register it
     elif (
-        env.scoring_datastore_access_key is not None
-        and containername is not None  # NOQA: E501
+            env.scoring_datastore_access_key is not None
+            and containername is not None  # NOQA: E501
     ):  # NOQA:E501
 
         datastore = Datastore.register_azure_blob_container(
@@ -85,11 +92,10 @@ def get_or_create_datastore(
             container_name=containername,
         )
     else:
-        raise ValueError(
-            "No existing datastore named {} nor was enough information supplied to create one.".format(  # NOQA: E501
-                datastorename
-            )
-        )
+        error = "No existing datastore named {} nor was enough " \
+                "information supplied to create one.".format(datastorename)
+        observability.log(description=error, severity=Severity.ERROR)
+        raise ValueError(error)
 
     return datastore
 
@@ -145,12 +151,11 @@ def get_fallback_input_dataset(ws: Workspace, env: Env) -> Dataset:
 
     if not os.path.exists(env.scoring_datastore_input_filename):
         error_message = (
-            "Could not find CSV dataset for scoring at {}. "
-            + "No alternate data store location was provided either.".format(
-                env.scoring_datastore_input_filename
-            )  # NOQA: E501
+                "Could not find CSV dataset for scoring at {}. "
+                + "No alternate data store location was provided either."
+                .format(env.scoring_datastore_input_filename)
         )
-
+        observability.log(description=error_message, severity=Severity.ERROR)
         raise FileNotFoundError(error_message)
 
     # upload the input data to the workspace default datastore
@@ -162,16 +167,18 @@ def get_fallback_input_dataset(ws: Workspace, env: Env) -> Dataset:
     )
 
     scoringinputds = (
-        Dataset.Tabular.from_delimited_files(scoreinputdataref)
-        .register(ws, env.scoring_dataset_name, create_new_version=True)
-        .as_named_input(env.scoring_dataset_name)
+        Dataset.Tabular.from_delimited_files(scoreinputdataref).register(
+            ws,
+            env.scoring_dataset_name,
+            create_new_version=True).as_named_input(
+            env.scoring_dataset_name)
     )
 
     return scoringinputds
 
 
 def get_output_location(
-    ws: Workspace, env: Env, outputdatastore: Datastore = None
+        ws: Workspace, env: Env, outputdatastore: Datastore = None
 ) -> PipelineData:
     """
     Returns a Datastore wrapped as a PipelineData instance suitable
@@ -200,7 +207,7 @@ def get_output_location(
 
 
 def get_inputds_outputloc(
-    ws: Workspace, env: Env
+        ws: Workspace, env: Env
 ) -> Tuple[Dataset, PipelineData]:  # NOQA: E501
     """
     Prepare the input and output for the scoring step. Input is a tabular
@@ -234,7 +241,7 @@ def get_inputds_outputloc(
 
 
 def get_run_configs(
-    ws: Workspace, computetarget: ComputeTarget, env: Env
+        ws: Workspace, computetarget: ComputeTarget, env: Env
 ) -> Tuple[ParallelRunConfig, RunConfiguration]:
     """
     Creates the necessary run configurations required by the
@@ -281,13 +288,13 @@ def get_run_configs(
 
 
 def get_scoring_pipeline(
-    scoring_dataset: Dataset,
-    output_loc: PipelineData,
-    score_run_config: ParallelRunConfig,
-    copy_run_config: RunConfiguration,
-    computetarget: ComputeTarget,
-    ws: Workspace,
-    env: Env,
+        scoring_dataset: Dataset,
+        output_loc: PipelineData,
+        score_run_config: ParallelRunConfig,
+        copy_run_config: RunConfiguration,
+        computetarget: ComputeTarget,
+        ws: Workspace,
+        env: Env,
 ) -> Pipeline:
     """
     Creates the scoring pipeline.
@@ -418,9 +425,9 @@ def build_batchscore_pipeline():
         pipeline_id_string = "##vso[task.setvariable variable=pipeline_id;isOutput=true]{}".format(  # NOQA: E501
             published_pipeline.id
         )
-        print(pipeline_id_string)
+        observability.log(pipeline_id_string)
     except Exception as e:
-        print(e)
+        observability.log(description=e, severity=Severity.ERROR)
         exit(1)
 
 
diff --git a/ml_service/pipelines/diabetes_regression_build_train_pipeline.py b/ml_service/pipelines/diabetes_regression_build_train_pipeline.py
index 03937186..8d7dba6f 100644
--- a/ml_service/pipelines/diabetes_regression_build_train_pipeline.py
+++ b/ml_service/pipelines/diabetes_regression_build_train_pipeline.py
@@ -9,6 +9,11 @@
 from ml_service.util.manage_environment import get_environment
 import os
 
+from utils.logger.logger_interface import Severity
+from utils.logger.observability import Observability
+
+observability = Observability()
+
 
 def main():
     e = Env()
@@ -18,14 +23,14 @@ def main():
         subscription_id=e.subscription_id,
         resource_group=e.resource_group,
     )
-    print("get_workspace:")
-    print(aml_workspace)
+    observability.log("get_workspace:")
+    observability.log(aml_workspace)
 
     # Get Azure machine learning cluster
     aml_compute = get_compute(aml_workspace, e.compute_name, e.vm_size)
     if aml_compute is not None:
-        print("aml_compute:")
-        print(aml_compute)
+        observability.log("aml_compute:")
+        observability.log(aml_compute)
 
     # Create a reusable Azure ML environment
     environment = get_environment(
@@ -44,6 +49,9 @@ def main():
     run_config.environment.environment_variables[
         "DATASTORE_NAME"
     ] = datastore_name  # NOQA: E501
+    run_config.environment.environment_variables[
+        "APP_INSIGHTS_CONNECTION_STRING"
+    ] = e.app_insights_connection_string
 
     model_name_param = PipelineParameter(name="model_name", default_value=e.model_name)  # NOQA: E501
     dataset_version_param = PipelineParameter(
@@ -68,10 +76,12 @@ def main():
         file_name = "diabetes.csv"
 
         if not os.path.exists(file_name):
-            raise Exception(
-                'Could not find CSV dataset at "%s". If you have bootstrapped your project, you will need to provide a CSV.'  # NOQA: E501
-                % file_name
-            )  # NOQA: E501
+            error = (
+                        'Could not find CSV dataset at "%s". '
+                        'If you have bootstrapped your project, you will need to provide a CSV.'  # NOQA: E501
+                        % file_name)
+            observability.log(description=error, severity=Severity.ERROR)
+            raise Exception(error)  # NOQA: E501
 
         # Upload file to default datastore in workspace
         datatstore = Datastore.get(aml_workspace, datastore_name)
@@ -124,7 +134,7 @@ def main():
         runconfig=run_config,
         allow_reuse=True,
     )
-    print("Step Train created")
+    observability.log("Step Train created")
 
     evaluate_step = PythonScriptStep(
         name="Evaluate Model ",
@@ -140,7 +150,7 @@ def main():
         runconfig=run_config,
         allow_reuse=False,
     )
-    print("Step Evaluate created")
+    observability.log("Step Evaluate created")
 
     register_step = PythonScriptStep(
         name="Register Model ",
@@ -152,15 +162,16 @@ def main():
         runconfig=run_config,
         allow_reuse=False,
     )
-    print("Step Register created")
+    observability.log("Step Register created")
     # Check run_evaluation flag to include or exclude evaluation step.
     if (e.run_evaluation).lower() == "true":
-        print("Include evaluation step before register step.")
+        observability.log("Include evaluation step before register step.")
         evaluate_step.run_after(train_step)
         register_step.run_after(evaluate_step)
         steps = [train_step, evaluate_step, register_step]
     else:
-        print("Exclude evaluation step and directly run register step.")
+        observability.log("Exclude evaluation step and "
+                          "directly run register step.")
         register_step.run_after(train_step)
         steps = [train_step, register_step]
 
@@ -172,8 +183,8 @@ def main():
         description="Model training/retraining pipeline",
         version=e.build_id,
     )
-    print(f"Published pipeline: {published_pipeline.name}")
-    print(f"for build {published_pipeline.version}")
+    observability.log(f"Published pipeline: {published_pipeline.name}")
+    observability.log(f"for build {published_pipeline.version}")
 
 
 if __name__ == "__main__":
diff --git a/ml_service/pipelines/diabetes_regression_build_train_pipeline_with_r.py b/ml_service/pipelines/diabetes_regression_build_train_pipeline_with_r.py
index 254f22eb..626ff1ca 100644
--- a/ml_service/pipelines/diabetes_regression_build_train_pipeline_with_r.py
+++ b/ml_service/pipelines/diabetes_regression_build_train_pipeline_with_r.py
@@ -6,6 +6,10 @@
 from ml_service.util.env_variables import Env
 from ml_service.util.manage_environment import get_environment
 
+from utils.logger.observability import Observability
+
+observability = Observability()
+
 
 def main():
     e = Env()
@@ -15,14 +19,14 @@ def main():
         subscription_id=e.subscription_id,
         resource_group=e.resource_group,
     )
-    print("get_workspace:")
-    print(aml_workspace)
+    observability.log("get_workspace:")
+    observability.log(aml_workspace)
 
     # Get Azure machine learning cluster
     aml_compute = get_compute(aml_workspace, e.compute_name, e.vm_size)
     if aml_compute is not None:
-        print("aml_compute:")
-        print(aml_compute)
+        observability.log("aml_compute:")
+        observability.log(aml_compute)
 
     # Create a reusable Azure ML environment
     # Make sure to include `r-essentials'
@@ -44,7 +48,7 @@ def main():
         runconfig=run_config,
         allow_reuse=False,
     )
-    print("Step Train created")
+    observability.log("Step Train created")
 
     steps = [train_step]
 
@@ -55,8 +59,8 @@ def main():
         description="Model training/retraining pipeline",
         version=e.build_id,
     )
-    print(f"Published pipeline: {published_pipeline.name}")
-    print(f"for build {published_pipeline.version}")
+    observability.log(f"Published pipeline: {published_pipeline.name}")
+    observability.log(f"for build {published_pipeline.version}")
 
 
 if __name__ == "__main__":
diff --git a/ml_service/pipelines/diabetes_regression_build_train_pipeline_with_r_on_dbricks.py b/ml_service/pipelines/diabetes_regression_build_train_pipeline_with_r_on_dbricks.py
index ae607b3b..00b09853 100644
--- a/ml_service/pipelines/diabetes_regression_build_train_pipeline_with_r_on_dbricks.py
+++ b/ml_service/pipelines/diabetes_regression_build_train_pipeline_with_r_on_dbricks.py
@@ -4,6 +4,10 @@
 from azureml.pipeline.steps import DatabricksStep
 from ml_service.util.env_variables import Env
 
+from utils.logger.observability import Observability
+
+observability = Observability()
+
 
 def main():
     e = Env()
@@ -13,8 +17,8 @@ def main():
         subscription_id=e.subscription_id,
         resource_group=e.resource_group
     )
-    print("get_workspace:")
-    print(aml_workspace)
+    observability.log("get_workspace:")
+    observability.log(aml_workspace)
 
     # Get Azure machine learning cluster
     aml_compute = get_compute(
@@ -22,8 +26,8 @@ def main():
         e.compute_name,
         e.vm_size)
     if aml_compute is not None:
-        print("aml_compute:")
-        print(aml_compute)
+        observability.log("aml_compute:")
+        observability.log(aml_compute)
 
     train_step = DatabricksStep(
         name="DBPythonInLocalMachine",
@@ -36,7 +40,7 @@ def main():
         allow_reuse=False
     )
 
-    print("Step Train created")
+    observability.log("Step Train created")
 
     steps = [train_step]
 
@@ -47,8 +51,8 @@ def main():
         description="Model training/retraining pipeline",
         version=e.build_id
     )
-    print(f'Published pipeline: {published_pipeline.name}')
-    print(f'for build {published_pipeline.version}')
+    observability.log(f'Published pipeline: {published_pipeline.name}')
+    observability.log(f'for build {published_pipeline.version}')
 
 
 if __name__ == '__main__':
diff --git a/ml_service/pipelines/diabetes_regression_verify_train_pipeline.py b/ml_service/pipelines/diabetes_regression_verify_train_pipeline.py
new file mode 100644
index 00000000..e69de29b
diff --git a/ml_service/pipelines/run_parallel_batchscore_pipeline.py b/ml_service/pipelines/run_parallel_batchscore_pipeline.py
index c046eb9c..5af6b3f9 100644
--- a/ml_service/pipelines/run_parallel_batchscore_pipeline.py
+++ b/ml_service/pipelines/run_parallel_batchscore_pipeline.py
@@ -30,6 +30,11 @@
 from azureml.pipeline.core import PublishedPipeline
 import argparse
 
+from utils.logger.logger_interface import Severity
+from utils.logger.observability import Observability
+
+observability = Observability()
+
 
 def parse_args():
     parser = argparse.ArgumentParser()
@@ -47,9 +52,10 @@ def get_pipeline(pipeline_id, ws: Workspace, env: Env):
         ]  # noqa E501
 
         if scoringpipelinelist.count == 0:
-            raise Exception(
-                "No pipeline found matching name:{}".format(env.scoring_pipeline_name)  # NOQA: E501
-            )
+            error = "No pipeline found matching name:{}".\
+                format(env.scoring_pipeline_name)
+            observability.log(description=error, severity=Severity.ERROR)
+            raise Exception(error)
         else:
             # latest published
             scoringpipeline = scoringpipelinelist[0]
@@ -127,7 +133,7 @@ def run_batchscore_pipeline():
             copy_output(list(run.get_steps())[0].id, env)
 
     except Exception as ex:
-        print("Error: {}".format(ex))
+        observability.log(description=ex, severity=Severity.ERROR)
 
 
 if __name__ == "__main__":
diff --git a/ml_service/pipelines/run_train_pipeline.py b/ml_service/pipelines/run_train_pipeline.py
index b68b9a15..457a9d81 100644
--- a/ml_service/pipelines/run_train_pipeline.py
+++ b/ml_service/pipelines/run_train_pipeline.py
@@ -3,9 +3,13 @@
 import argparse
 from ml_service.util.env_variables import Env
 
+from utils.logger.logger_interface import Severity
+from utils.logger.observability import Observability
+
+observability = Observability()
 
-def main():
 
+def main():
     parser = argparse.ArgumentParser("register")
     parser.add_argument(
         "--output_pipeline_id_file",
@@ -38,22 +42,28 @@ def main():
             if p.version == e.build_id:
                 matched_pipes.append(p)
 
-    if(len(matched_pipes) > 1):
+    if (len(matched_pipes) > 1):
         published_pipeline = None
-        raise Exception(f"Multiple active pipelines are published for build {e.build_id}.")  # NOQA: E501
-    elif(len(matched_pipes) == 0):
+        error = f"Multiple active pipelines are published for build {e.build_id}."  # NOQA: E501
+        observability.log(description=error, severity=Severity.ERROR)
+        raise Exception(error)
+    elif (len(matched_pipes) == 0):
         published_pipeline = None
-        raise KeyError(f"Unable to find a published pipeline for this build {e.build_id}")  # NOQA: E501
+        error = f"Unable to find a published pipeline for this build " \
+            f"{e.build_id}"
+        observability.log(description=error, severity=Severity.ERROR)
+        raise KeyError(error)  # NOQA: E501
     else:
         published_pipeline = matched_pipes[0]
-        print("published pipeline id is", published_pipeline.id)
+        observability.log("published pipeline id is"
+                          + str(published_pipeline.id))
 
         # Save the Pipeline ID for other AzDO jobs after script is complete
         if args.output_pipeline_id_file is not None:
             with open(args.output_pipeline_id_file, "w") as out_file:
                 out_file.write(published_pipeline.id)
 
-        if(args.skip_train_execution is False):
+        if (args.skip_train_execution is False):
             pipeline_parameters = {"model_name": e.model_name}
             tags = {"BuildId": e.build_id}
             if (e.build_uri is not None):
@@ -66,7 +76,7 @@ def main():
                 tags=tags,
                 pipeline_parameters=pipeline_parameters)
 
-            print("Pipeline run initiated ", run.id)
+            observability.log("Pipeline run initiated " + str(run.id))
 
 
 if __name__ == "__main__":
diff --git a/ml_service/util/attach_compute.py b/ml_service/util/attach_compute.py
index ad9668db..bf0f61e2 100644
--- a/ml_service/util/attach_compute.py
+++ b/ml_service/util/attach_compute.py
@@ -1,17 +1,21 @@
-
 from azureml.core import Workspace
 from azureml.core.compute import AmlCompute
 from azureml.core.compute import ComputeTarget
 from azureml.exceptions import ComputeTargetException
 from ml_service.util.env_variables import Env
 
+from utils.logger.logger_interface import Severity
+from utils.logger.observability import Observability
+
+observability = Observability()
+
 
 def get_compute(workspace: Workspace, compute_name: str, vm_size: str, for_batch_scoring: bool = False):  # NOQA E501
     try:
         if compute_name in workspace.compute_targets:
             compute_target = workspace.compute_targets[compute_name]
             if compute_target and type(compute_target) is AmlCompute:
-                print("Found existing compute target " + compute_name + " so using it.") # NOQA
+                observability.log("Found existing compute target " + compute_name + " so using it.")  # NOQA
         else:
             e = Env()
             compute_config = AmlCompute.provisioning_configuration(
@@ -33,6 +37,7 @@ def get_compute(workspace: Workspace, compute_name: str, vm_size: str, for_batch
             )
         return compute_target
     except ComputeTargetException as ex:
-        print(ex)
-        print("An error occurred trying to provision compute.")
+        observability.log(ex, severity=Severity.ERROR)
+        observability.log("An error occurred trying to provision compute.",
+                          severity=Severity.ERROR)
         exit(1)
diff --git a/ml_service/util/create_scoring_image.py b/ml_service/util/create_scoring_image.py
index 378cb3b4..82e1d37b 100644
--- a/ml_service/util/create_scoring_image.py
+++ b/ml_service/util/create_scoring_image.py
@@ -6,6 +6,11 @@
 import shutil
 from ml_service.util.env_variables import Env
 
+from utils.logger.logger_interface import Severity
+from utils.logger.observability import Observability
+
+observability = Observability()
+
 e = Env()
 
 # Get Azure machine learning workspace
@@ -27,7 +32,7 @@
 model = Model(ws, name=e.model_name, version=e.model_version)
 sources_dir = e.sources_directory_train
 if (sources_dir is None):
-    sources_dir = 'diabetes_regression'
+    sources_dir = '.'
 score_script = os.path.join(".", sources_dir, e.score_script)
 score_file = os.path.basename(score_script)
 path_to_scoring = os.path.dirname(score_script)
@@ -43,17 +48,21 @@
 package = Model.package(ws, [model], inference_config)
 package.wait_for_creation(show_output=True)
 # Display the package location/ACR path
-print(package.location)
+observability.log(package.location)
 
 os.chdir(cwd)
 
 if package.state != "Succeeded":
-    raise Exception("Image creation status: {package.creation_state}")
+    error = "Image creation status: {package.creation_state}"
+    observability.log(description=error, severity=Severity.ERROR)
+    raise Exception(error)
 
-print("Package stored at {} with build log {}".format(package.location, package.package_build_log_uri))  # NOQA: E501
+observability.log(
+    "Package stored at {} with build log {}".format(package.location, package.package_build_log_uri))  # NOQA: E501
 
 # Save the Image Location for other AzDO jobs after script is complete
 if args.output_image_location_file is not None:
-    print("Writing image location to %s" % args.output_image_location_file)
+    observability.log("Writing image location to %s"
+                      % args.output_image_location_file)
     with open(args.output_image_location_file, "w") as out_file:
         out_file.write(str(package.location))
diff --git a/ml_service/util/env_variables.py b/ml_service/util/env_variables.py
index 753c152d..9252de75 100644
--- a/ml_service/util/env_variables.py
+++ b/ml_service/util/env_variables.py
@@ -124,3 +124,6 @@ class Env:
     batchscore_copy_script_path: Optional[str] = os.environ.get(
         "BATCHSCORE_COPY_SCRIPT_PATH"
     )  # NOQA: E501
+    app_insights_connection_string: Optional[str] = os.environ.get(
+        "APP_INSIGHTS_CONNECTION_STRING"
+    )  # NOQA: E501
diff --git a/ml_service/util/manage_environment.py b/ml_service/util/manage_environment.py
index 54c5a72f..ed4b8547 100644
--- a/ml_service/util/manage_environment.py
+++ b/ml_service/util/manage_environment.py
@@ -1,17 +1,21 @@
-
 import os
 from azureml.core import Workspace, Environment
 from ml_service.util.env_variables import Env
 from azureml.core.runconfig import DEFAULT_CPU_IMAGE, DEFAULT_GPU_IMAGE
 
+from utils.logger.logger_interface import Severity
+from utils.logger.observability import Observability
+
+observability = Observability()
+
 
 def get_environment(
-    workspace: Workspace,
-    environment_name: str,
-    conda_dependencies_file: str,
-    create_new: bool = False,
-    enable_docker: bool = None,
-    use_gpu: bool = False
+        workspace: Workspace,
+        environment_name: str,
+        conda_dependencies_file: str,
+        create_new: bool = False,
+        enable_docker: bool = None,
+        use_gpu: bool = False
 ):
     try:
         e = Env()
@@ -24,7 +28,7 @@ def get_environment(
         if restored_environment is None or create_new:
             new_env = Environment.from_conda_specification(
                 environment_name,
-                os.path.join(e.sources_directory_train, conda_dependencies_file),  # NOQA: E501
+                os.path.join(e.sources_directory_train, "diabetes_regression/" + conda_dependencies_file),  # NOQA: E501
             )  # NOQA: E501
             restored_environment = new_env
             if enable_docker is not None:
@@ -33,8 +37,8 @@ def get_environment(
             restored_environment.register(workspace)
 
         if restored_environment is not None:
-            print(restored_environment)
+            observability.log(restored_environment)
         return restored_environment
     except Exception as e:
-        print(e)
+        observability.log(description=e, severity=Severity.ERROR)
         exit(1)
diff --git a/ml_service/util/smoke_test_scoring_service.py b/ml_service/util/smoke_test_scoring_service.py
index 0fa34b1e..8307a865 100644
--- a/ml_service/util/smoke_test_scoring_service.py
+++ b/ml_service/util/smoke_test_scoring_service.py
@@ -6,6 +6,9 @@
 from ml_service.util.env_variables import Env
 import secrets
 
+from utils.logger.observability import Observability
+
+observability = Observability()
 
 input = {"data": [[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
                   [10, 9, 8, 7, 6, 5, 4, 3, 2, 1]]}
@@ -18,7 +21,7 @@ def call_web_service(e, service_type, service_name):
         subscription_id=e.subscription_id,
         resource_group=e.resource_group
     )
-    print("Fetching service")
+    observability.log("Fetching service")
     headers = {}
     if service_type == "ACI":
         service = AciWebservice(aml_workspace, service_name)
@@ -27,8 +30,8 @@ def call_web_service(e, service_type, service_name):
     if service.auth_enabled:
         service_keys = service.get_keys()
         headers['Authorization'] = 'Bearer ' + service_keys[0]
-    print("Testing service")
-    print(". url: %s" % service.scoring_uri)
+    observability.log("Testing service")
+    observability.log(". url: %s" % service.scoring_uri)
     output = call_web_app(service.scoring_uri, headers)
 
     return output
@@ -51,8 +54,8 @@ def call_web_app(url, headers):
         except requests.exceptions.HTTPError as e:
             if i == retries - 1:
                 raise e
-            print(e)
-            print("Retrying...")
+            observability.log(e)
+            observability.log("Retrying...")
             time.sleep(1)
 
 
@@ -80,11 +83,11 @@ def main():
         output = call_web_app(args.service, {})
     else:
         output = call_web_service(e, args.type, args.service)
-    print("Verifying service output")
+    observability.log("Verifying service output")
 
     assert "result" in output
     assert len(output["result"]) == output_len
-    print("Smoke test successful.")
+    observability.log("Smoke test successful.")
 
 
 if __name__ == '__main__':
diff --git a/utils/__init__.py b/utils/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/utils/images/Metrics.png b/utils/images/Metrics.png
new file mode 100644
index 00000000..88b7cf0b
Binary files /dev/null and b/utils/images/Metrics.png differ
diff --git a/utils/images/logs-aml.png b/utils/images/logs-aml.png
new file mode 100644
index 00000000..24b6ef8d
Binary files /dev/null and b/utils/images/logs-aml.png differ
diff --git a/utils/images/logs-appinsights.png b/utils/images/logs-appinsights.png
new file mode 100644
index 00000000..c06f0d9c
Binary files /dev/null and b/utils/images/logs-appinsights.png differ
diff --git a/utils/images/metrics-appinsights.png b/utils/images/metrics-appinsights.png
new file mode 100644
index 00000000..c7a6ba15
Binary files /dev/null and b/utils/images/metrics-appinsights.png differ
diff --git a/utils/logger/__init__.py b/utils/logger/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/utils/logger/app_insights_logger.py b/utils/logger/app_insights_logger.py
new file mode 100644
index 00000000..f0d799c2
--- /dev/null
+++ b/utils/logger/app_insights_logger.py
@@ -0,0 +1,130 @@
+import logging
+import time
+import uuid
+
+from opencensus.ext.azure import metrics_exporter
+from opencensus.ext.azure.log_exporter import AzureLogHandler
+
+
+from opencensus.stats import aggregation as aggregation_module
+from opencensus.stats import measure as measure_module
+from opencensus.stats import stats as stats_module
+from opencensus.stats import view as view_module
+from opencensus.tags import tag_map as tag_map_module
+
+from ml_service.util.env_variables import Env
+from utils.logger.logger_interface import (
+    LoggerInterface,
+    ObservabilityAbstract,
+    Severity,
+)
+
+
+class AppInsightsLogger(LoggerInterface, ObservabilityAbstract):
+    def __init__(self, run, export_interval):
+        self.env = Env()
+        # initializes log exporter
+        handler = AzureLogHandler(
+            connection_string=self.env.app_insights_connection_string,
+            logging_sampling_rate=1.0,
+        )
+        handler.add_telemetry_processor(self.callback_function)
+        self.run_id = self.get_run_id(run)
+        self.logger = logging.getLogger(__name__)
+        self.logger.addHandler(handler)
+        # initializes metric exporter
+        self.export_interval = export_interval
+        exporter = metrics_exporter.new_metrics_exporter(
+            enable_standard_metrics=False,
+            export_interval=self.export_interval,
+            connection_string=self.env.app_insights_connection_string,
+        )
+        exporter.add_telemetry_processor(self.callback_function)
+        stats_module.stats.view_manager.register_exporter(exporter)
+
+    def log_metric(
+        self, name="", value="", description="", log_parent=False,
+    ):
+        """
+        Sends a custom metric to appInsights
+        :param name: name  of the metric
+        :param value: value of the metric
+        :param description: description of the metric
+        :param log_parent: not being used for this logger
+        :return:
+        """
+        measurement_map = \
+            stats_module.stats.stats_recorder.new_measurement_map()
+        tag_map = tag_map_module.TagMap()
+
+        measure = measure_module.MeasureFloat(name, description)
+        self.set_view(name, description, measure)
+        measurement_map.measure_float_put(measure, value)
+        measurement_map.record(tag_map)
+        # Default export interval is every 15.0s
+        # Your application should run for at least this amount
+        # of time so the exporter will meet this interval
+        # Sleep can fulfill this https://pypi.org/project/opencensus-ext-azure/
+        time.sleep(self.export_interval)
+
+    def log(self, description="", severity=Severity.WARNING):
+        """
+        Sends the logs to App Insights
+        :param description: log description
+        :param severity: log severity
+        :return:
+        """
+
+        if severity == self.severity.DEBUG:
+            self.logger.debug(description)
+        elif severity == self.severity.INFO:
+            self.logger.info(description)
+        elif severity == self.severity.WARNING:
+            self.logger.warning(description)
+        elif severity == self.severity.ERROR:
+            self.logger.error(description)
+        elif severity == self.severity.CRITICAL:
+            self.logger.critical(description)
+
+    def get_run_id(self, run):
+        """
+        gets the correlation ID by the in following order:
+        - If the script is running  in an Online run Context of AML --> run_id
+        - If the script is running where a build_id
+        environment variable  is set --> build_id
+        - Else --> generate a unique id
+        :param run:
+        :return: correlation_id
+        """
+        run_id = str(uuid.uuid1())
+        if not run.id.startswith(self.OFFLINE_RUN):
+            run_id = run.id
+        elif self.env.build_id:
+            run_id = self.env.build_id
+        return run_id
+
+    @staticmethod
+    def set_view(metric, description, measure):
+        """
+        Sets the view for the custom metric
+        """
+        prompt_view = view_module.View(
+            metric,
+            description,
+            [],
+            measure,
+            aggregation_module.LastValueAggregation()
+        )
+        stats_module.stats.view_manager.register_view(prompt_view)
+
+    def callback_function(self, envelope):
+        """
+        Attaches a correlation_id as a custom
+        dimension to the exporter just before
+        sending the logs/metrics
+        :param envelope:
+        :return: Always return True
+        (if False, it  does not export metrics/logs)
+        """
+        envelope.data.baseData.properties[self.CORRELATION_ID] = self.run_id
+        return True
diff --git a/utils/logger/azure_ml_logger.py b/utils/logger/azure_ml_logger.py
new file mode 100644
index 00000000..64b8d22e
--- /dev/null
+++ b/utils/logger/azure_ml_logger.py
@@ -0,0 +1,49 @@
+import datetime
+import time
+
+from utils.logger.logger_interface import (
+    LoggerInterface,
+    ObservabilityAbstract,
+    Severity,
+)
+
+
+class AzureMlLogger(LoggerInterface, ObservabilityAbstract):
+    def __init__(self, run=None):
+        self.run = run
+
+    def log_metric(self, name, value, description, log_parent):
+        """Log a metric value to the run with the given name.
+        :param log_parent: mark True  if you want to log to parent Run
+        :param name: The name of metric.
+        :type name: str
+        :param value: The value to be posted to the service.
+        :type value:
+        :param description: An optional metric description.
+        :type description: str
+        """
+        if name != "" and value != "":
+            self.run.log(
+                name, value, description
+            ) if log_parent is False \
+                else self.run.parent.log(name, value, description)
+
+    def log(self, description="", severity=Severity.WARNING):
+        """
+        Sends the logs to AML (experiments -> logs/outputs)
+        :param description: log description
+        :param severity: log severity
+        :return:
+        """
+
+        time_stamp = datetime.datetime.fromtimestamp(time.time()).strftime(
+            "%Y-%m-%d %H:%M:%S"
+        )
+        callee = self.get_callee(
+            2
+        )  # to get the script who is calling Observability
+        print(
+            "{}, [{}], {}:{}".format(
+                time_stamp, self.severity_map[severity], callee, description
+            )
+        )
diff --git a/utils/logger/logger_interface.py b/utils/logger/logger_interface.py
new file mode 100644
index 00000000..f7402962
--- /dev/null
+++ b/utils/logger/logger_interface.py
@@ -0,0 +1,41 @@
+import inspect
+
+
+class Severity:
+    DEBUG = 10
+    INFO = 20
+    WARNING = 30
+    ERROR = 40
+    CRITICAL = 50
+
+
+class LoggerInterface:
+    def log_metric(self, name, value, description, log_parent):
+        pass
+
+    def log(self, name, value, description, severity, log_parent):
+        pass
+
+
+class ObservabilityAbstract:
+    OFFLINE_RUN = "OfflineRun"
+    CORRELATION_ID = "correlation_id"
+    severity = Severity()
+    severity_map = {10: "DEBUG", 20: "INFO",
+                    30: "WARNING", 40: "ERROR", 50: "CRITICAL"}
+
+    @staticmethod
+    def get_callee(stack_level):
+        """
+        This method get the callee location in [file_name:line_number] format
+        :param stack_level:
+        :return: string of [file_name:line_number]
+        """
+        try:
+            stack = inspect.stack()
+            file_name = stack[stack_level + 1].filename.split("/")[-1]
+            line_number = stack[stack_level + 1].lineno
+            return "{}:{}".format(file_name, line_number)
+        except IndexError:
+            print("Index error, failed to log to AzureML")
+            return ""
diff --git a/utils/logger/observability.py b/utils/logger/observability.py
new file mode 100644
index 00000000..9a86c25b
--- /dev/null
+++ b/utils/logger/observability.py
@@ -0,0 +1,81 @@
+from azureml.core import Run
+
+from ml_service.util.env_variables import Env
+from utils.logger.app_insights_logger import AppInsightsLogger
+from utils.logger.azure_ml_logger import AzureMlLogger
+from utils.logger.logger_interface import (
+    ObservabilityAbstract,
+    LoggerInterface,
+    Severity,
+)
+
+
+class Loggers(ObservabilityAbstract):
+    def __init__(self, export_interval) -> None:
+        self.loggers: LoggerInterface = []
+        self.register_loggers(export_interval)
+
+    def add(self, logger) -> None:
+        self.loggers.append(logger)
+
+    def get_loggers_string(self) -> None:
+        return ", ".join([type(x).__name__ for x in self.loggers])
+
+    def register_loggers(self, export_interval):
+        """
+        This method is responsible to create loggers/tracers
+        and add them to the list of loggers
+        Notes:
+        - If the context of the Run object is offline,
+        we do not create AzureMlLogger instance
+        - If APP_INSIGHTS_CONNECTION_STRING is notset
+        to ENV variable, we do not create AppInsightsLogger
+        instance
+        """
+        run = Run.get_context()
+        if not run.id.startswith(self.OFFLINE_RUN):
+            self.loggers.append(AzureMlLogger(run))
+        if Env().app_insights_connection_string:
+            self.loggers.append(AppInsightsLogger(run, export_interval))
+
+
+class Observability(LoggerInterface):
+    def __init__(self, export_interval=15) -> None:
+        self._loggers = Loggers(export_interval)
+
+    def log_metric(
+            self, name="", value="", description="", log_parent=False,
+    ):
+        """
+        this method sends the metrics to all registered loggers
+        :param name: metric name
+        :param value: metric value
+        :param description: description of the metric
+        :param log_parent: (only for AML), send the metric to the run.parent
+        :return:
+        """
+        for logger in self._loggers.loggers:
+            logger.log_metric(name, value, description, log_parent)
+
+    def log(self, description="", severity=Severity.WARNING):
+        """
+        this method sends the logs to all registered loggers
+        :param description: Actual log description to be sent
+        :param severity: log Severity
+        :return:
+        """
+        for logger in self._loggers.loggers:
+            logger.log(description, severity)
+
+    def get_logger(self, logger_class):
+        """
+        This method iterate over the loggers and it
+        returns the logger with the same type as the provided one.
+        this is a reference that can be used in case
+        any of the built in functions of the loggers is required
+        :param logger_class:
+        :return: a logger class
+        """
+        for logger in self._loggers.loggers:
+            if type(logger) is type(logger_class):
+                return logger
diff --git a/utils/observability-usage.md b/utils/observability-usage.md
new file mode 100644
index 00000000..9e92e96a
--- /dev/null
+++ b/utils/observability-usage.md
@@ -0,0 +1,130 @@
+## Observability Usage
+
+To use the logger in your project: 
+
+**1. dependencies:**
+
+Observability requires the following dependencies. Make sure you add them into your project before the usage:
+```yaml
+      - opencensus==0.7.7
+      - opencensus-context==0.1.1
+      - opencensus-ext-azure==1.0.2
+```  
+Additionally, AppInsightsLogger needs the connection to AppInsights. Therefore make sure you add the Instrumentation key to the environment variables of the running environment:
+```python
+os.environ["APP_INSIGHTS_CONNECTION_STRING"] = "_____"
+```
+ 
+
+**2. Import Observability** 
+
+Import Observability object in your script and create an object of Observability type.
+Observability object, checks the context of the 
+[Run](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.run(class)?view=azure-ml-py) object and if
+ the context is **Online**, it will add **AzureMlLogger** to the list of loggers.
+If  the Run context is offline, it will only add AppInsightLogger to list of loggers. 
+```python
+from util.logger.observability import Observability
+
+observability = Observability()
+```
+
+**3. Log your message/metrics**
+
+
+**3.1 log metrics:**
+Below is the usage of **log_metric** method.
+```python
+observability.log_metric(name="alpha", value="2.1", description="value of alpha")
+observability.log_metric(name="alpha", value="2.1", description="value of alpha", log_parent=True)
+```
+upon a call to _log_metric_:
+- If the context of Run class is online, metrics will be sent to azure ML.
+- Metrics will be sent to AppInsights as a custom metrics. 
+Please note that metrics exporter sends the metrics at **export_interval** rate (in seconds) to 
+AppInsights based on their last entered value. The default export_interval is 15 seconds.
+
+
+**3.2 log messages:**
+Below is the usage of **log** method.
+
+
+```python
+observability.log(description="value of alpha",severity=Severity.WARNING)
+```
+upon a call to _log_metric_:
+- If the context of Run class is online, logs will be sent to azure ML.
+- Logs will be sent to AppInsights. 
+
+
+
+**4. check the logs/metrics**
+
+Observability is sending the logs/metrics to AzureMl( in online context) and to AppInsights.
+
+**Logs/Metrics Correlation:** 
+
+The correlation_id is used to map specific Run/Build with the logs/metrics being sent  to different resources. 
+Correlation_id will be created and added to telemetry processor in a custom dimension in following steps:
+
+1. If the logging is happening in an **Online Run Context**,
+the correlationId is similar to **RunId**
+
+1. If the logging is happening in an **Offline Run Context**,
+the correlationId is the same as the **buildId** of the Pipeline
+(fetched from EnvVariable)
+
+1. If the logging is happening in Offline Run Context and there
+is no BuildId set to the Environment variable, it will will
+be associated with a **unique identifier**.
+
+**Check Metrics in Azure ML:**
+
+To check the metrics in Azure ML, navigate to [Azure ML portal](https://ml.azure.com/), find your desired experiment
+ and in the Metrics tab  you can see the logged metrics. 
+ ![Metrcis](images/Metrics.png)
+ 
+**Check Metrics in  AppInsights:**
+
+To check the metrics in Application Insights, navigate to Azure Portal and your application Insights service. 
+Click on Logs tab and select custom Metrics. You may use the below queries to retrieve your metrics:
+The following lists all the custom metrics:
+```sql
+customMetrics
+```
+To narrow your search to the  specific run you can provide the correlation_id:
+
+```sql
+customMetrics 
+| where customDimensions.correlation_id contains "e56b31b7-513f-4c34-9158-c2e1b28a5aaf" 
+```
+![metrics-appInsights](images/metrics-appinsights.png)
+
+**Check logs in  Azure ML:**
+
+Logs will be sent to AzureML only in Online context. 
+You can check the logs by logging in to [Azure ML portal](https://ml.azure.com/) portal. 
+Then click on desired experiment and select the specific step.
+click on logs/output tab and check your logs.
+logs will be sent to Azure ML in the following format: 
+```text
+timeStamp, [severity], callee_file_name:line_number:description
+```
+![logs-aml](images/logs-aml.png)
+
+**Check logs in  Application Insights:**
+
+To check the logs in Application Insights, navigate to Azure Portal and your application Insights service. 
+Click on Logs tab and select traces. You may use the below queries to retrieve your logs:
+The following lists all the custom metrics:
+```sql
+traces
+```
+To narrow your search to the specific run you can provide the correlation_id:
+
+```sql
+traces
+| where customDimensions.correlation_id contains "e56b31b7-513f-4c34-9158-c2e1b28a5aaf"
+```
+![logs-appInsights](images/logs-appinsights.png)
+
diff --git a/utils/tests/__init__.py b/utils/tests/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/utils/tests/test_app_insights_logger.py b/utils/tests/test_app_insights_logger.py
new file mode 100644
index 00000000..5f54fb6a
--- /dev/null
+++ b/utils/tests/test_app_insights_logger.py
@@ -0,0 +1,72 @@
+import logging
+import unittest
+from unittest.mock import patch
+
+from utils.logger.app_insights_logger import AppInsightsLogger
+
+
+class RealAppInsightsLogger(AppInsightsLogger):
+    def __init__(self):
+        self.logger = logging.getLogger(__name__)
+        self.env = MockEnv("")
+
+
+class MockRun:
+    def __init__(self, run_id):
+        self.id = run_id
+
+
+class MockEnv:
+    def __init__(self, run_id):
+        self.build_id = run_id
+
+
+class TestObservability(unittest.TestCase):
+    @patch("utils.logger.app_insights_logger.AppInsightsLogger")
+    def setUp(cls, mock_app_insights_logger):
+        cls.concert_app_insights_logger = RealAppInsightsLogger()
+        cls.mock_app_insights_logger = mock_app_insights_logger
+
+    def test_get_run_id_having_online_context(self):
+        expected = "FOO"
+
+        response = self.concert_app_insights_logger.get_run_id(MockRun("FOO"))
+
+        self.assertEqual(expected, response)
+
+    def test_get_run_id_having_online_context_using_build_id(self):
+        self.concert_app_insights_logger.env.build_id = expected = "FOO"
+
+        response = self.concert_app_insights_logger.\
+            get_run_id(MockRun("OfflineRun"))
+
+        self.assertEqual(expected, response)
+
+    def test_get_run_id_having_online_context_using_uuid(self):
+        self.concert_app_insights_logger.env.build_id = ""
+
+        response = self.concert_app_insights_logger.\
+            get_run_id(MockRun("OfflineRun"))
+
+        self.assertIsNotNone(response)
+
+    def test_log_called_with_parameters(self):
+        self.mock_app_insights_logger.log("FOO", "BAZ")
+
+        self.mock_app_insights_logger.log.assert_called_with("FOO", "BAZ")
+
+    def test_log_metric_called_with_parameters(self):
+        self.mock_app_insights_logger.log_metric("FOO", "BAZ", "BAR", False)
+
+        self.mock_app_insights_logger.log_metric.assert_called_with(
+            "FOO", "BAZ", "BAR", False
+        )
+
+    def test_set_view_is_called_with_parameters(self):
+        self.mock_app_insights_logger.set_view("FOO", "BAR", "BAZ")
+        self.mock_app_insights_logger.set_view.\
+            assert_called_with("FOO", "BAR", "BAZ")
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/utils/tests/test_azure_ml_logger.py b/utils/tests/test_azure_ml_logger.py
new file mode 100644
index 00000000..babdd62f
--- /dev/null
+++ b/utils/tests/test_azure_ml_logger.py
@@ -0,0 +1,32 @@
+import unittest
+from unittest.mock import patch
+
+from utils.logger.azure_ml_logger import AzureMlLogger
+
+
+class TestObservability(unittest.TestCase):
+    @patch("utils.logger.azure_ml_logger.AzureMlLogger")
+    def setUp(cls, mock_azure_ml_logger):
+        cls.azure_ml_logger = mock_azure_ml_logger
+
+    def test_log_called_with_parameters(self):
+        self.azure_ml_logger.log("FOO", "BAZ")
+
+        self.azure_ml_logger.log.assert_called_with("FOO", "BAZ")
+
+    def test_log_metric_called_with_parameters(self):
+        self.azure_ml_logger.log_metric("FOO", "BAZ", "BAR")
+
+        self.azure_ml_logger.log_metric.assert_called_with("FOO", "BAZ", "BAR")
+
+    def test_get_callee_returns_callee_file_with_line_number(self):
+        azure_ml_logger = AzureMlLogger()
+        expected = "test_azure_ml_logger.py:26"
+
+        response = azure_ml_logger.get_callee(0)
+
+        self.assertEqual(expected, response)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/utils/tests/test_observability.py b/utils/tests/test_observability.py
new file mode 100644
index 00000000..17129d59
--- /dev/null
+++ b/utils/tests/test_observability.py
@@ -0,0 +1,55 @@
+import unittest
+from unittest.mock import patch
+
+from utils.logger.observability import Observability
+
+
+class ObservabilityMock(Observability):
+    @patch("utils.logger.app_insights_logger.AppInsightsLogger")
+    @patch("utils.logger.azure_ml_logger.AzureMlLogger")
+    @patch("utils.logger.observability.Loggers")
+    def __init__(self, mock_loggers, mock_aml_logger, mock_app_insight_logger):
+        mock_loggers.loggers = [mock_aml_logger, mock_app_insight_logger]
+        self._loggers = mock_loggers
+
+
+class TestObservability(unittest.TestCase):
+    @patch("utils.logger.observability.Observability")
+    def setUp(cls, mock_observability):
+        cls.observability = mock_observability
+
+    def test_log_metric_called_with_parameters(self):
+        self.observability.log_metric("FOO", "BAZ", "BAR")
+
+        self.observability.log_metric.assert_called_with("FOO", "BAZ", "BAR")
+
+    def test_log_called_with_parameters(self):
+        self.observability.log("FOO", "BAZ")
+
+        self.observability.log.assert_called_with("FOO", "BAZ")
+
+    def test_log_metric_is_being_called_by_all_loggers(self):
+        self.observability = ObservabilityMock()
+
+        self.observability.log_metric("FOO", "BAZ", "BAR")
+
+        self.observability._loggers.loggers[0].log_metric.assert_called_with(
+            "FOO", "BAZ", "BAR", False
+        )
+        self.observability._loggers.loggers[1].log_metric.assert_called_with(
+            "FOO", "BAZ", "BAR", False
+        )
+
+    def test_log_is_being_called_by_all_loggers(self):
+        self.observability = ObservabilityMock()
+
+        self.observability.log("FOO", "BAZ")
+
+        self.observability._loggers.loggers[0].\
+            log.assert_called_with("FOO", "BAZ")
+        self.observability._loggers.loggers[1].\
+            log.assert_called_with("FOO", "BAZ")
+
+
+if __name__ == "__main__":
+    unittest.main()