diff --git a/cookiecutter.json b/cookiecutter.json
deleted file mode 100644
index 8df2587f..00000000
--- a/cookiecutter.json
+++ /dev/null
@@ -1,3 +0,0 @@
-{
-  "__please_use_databricks_cli_for_project_creation": "Please refer to README.md"
-}
diff --git a/template/update_layout.tmpl b/template/update_layout.tmpl
index a666e23c..006aec52 100644
--- a/template/update_layout.tmpl
+++ b/template/update_layout.tmpl
@@ -35,22 +35,22 @@
 # Remove Delta and Feature Store code in cases of MLflow Recipes.
 {{ if (eq .input_include_mlflow_recipes `yes`) }}
     # delta_paths
-    {{ skip (printf `%s/%s/%s` $root_dir $project_name_alphanumeric_underscore `training/notebooks/Train.py`) }}
+    {{ skip (printf `%s/%s/%s` $root_dir $project_name_alphanumeric_underscore `training/Train.py`) }}
     # feature_store_paths
     {{ skip (printf `%s/%s/%s` $root_dir $project_name_alphanumeric_underscore `feature_engineering`) }}
     {{ skip (printf `%s/%s/%s` $root_dir $project_name_alphanumeric_underscore `tests/feature_engineering`) }}
-    {{ skip (printf `%s/%s/%s` $root_dir $project_name_alphanumeric_underscore `training/notebooks/TrainWithFeatureStore.py`) }}
+    {{ skip (printf `%s/%s/%s` $root_dir $project_name_alphanumeric_underscore `training/TrainWithFeatureStore.py`) }}
     {{ skip (printf `%s/%s/%s` $root_dir $project_name_alphanumeric_underscore `resources/feature-engineering-workflow-resource.yml`) }}
 # Remove Delta and MLflow Recipes code in cases of Feature Store.
 {{ else if (eq .input_include_feature_store `yes`) }}
     # delta_paths
-    {{ skip (printf `%s/%s/%s` $root_dir $project_name_alphanumeric_underscore `training/notebooks/Train.py`) }}
+    {{ skip (printf `%s/%s/%s` $root_dir $project_name_alphanumeric_underscore `training/Train.py`) }}
     # recipe_paths
     {{ skip (printf `%s/%s/%s` $root_dir $project_name_alphanumeric_underscore `training/profiles`) }}
     {{ skip (printf `%s/%s/%s` $root_dir $project_name_alphanumeric_underscore `training/steps`) }}
     {{ skip (printf `%s/%s/%s` $root_dir $project_name_alphanumeric_underscore `training/data`) }}
     {{ skip (printf `%s/%s/%s` $root_dir $project_name_alphanumeric_underscore `training/__init__.py`) }}
-    {{ skip (printf `%s/%s/%s` $root_dir $project_name_alphanumeric_underscore `training/notebooks/TrainWithMLflowRecipes.py`) }}
+    {{ skip (printf `%s/%s/%s` $root_dir $project_name_alphanumeric_underscore `training/TrainWithMLflowRecipes.py`) }}
     {{ skip (printf `%s/%s/%s` $root_dir $project_name_alphanumeric_underscore `training/recipe.yaml`) }}
     {{ skip (printf `%s/%s/%s` $root_dir $project_name_alphanumeric_underscore `training/README.md`) }}
     {{ skip (printf `%s/%s/%s` $root_dir $project_name_alphanumeric_underscore `tests/training/ingest_test.py`) }}
@@ -65,7 +65,7 @@
     {{ skip (printf `%s/%s/%s` $root_dir $project_name_alphanumeric_underscore `training/steps`) }}
     {{ skip (printf `%s/%s/%s` $root_dir $project_name_alphanumeric_underscore `training/data`) }}
     {{ skip (printf `%s/%s/%s` $root_dir $project_name_alphanumeric_underscore `training/__init__.py`) }}
-    {{ skip (printf `%s/%s/%s` $root_dir $project_name_alphanumeric_underscore `training/notebooks/TrainWithMLflowRecipes.py`) }}
+    {{ skip (printf `%s/%s/%s` $root_dir $project_name_alphanumeric_underscore `training/TrainWithMLflowRecipes.py`) }}
     {{ skip (printf `%s/%s/%s` $root_dir $project_name_alphanumeric_underscore `training/recipe.yaml`) }}
     {{ skip (printf `%s/%s/%s` $root_dir $project_name_alphanumeric_underscore `training/README.md`) }}
     {{ skip (printf `%s/%s/%s` $root_dir $project_name_alphanumeric_underscore `tests/training/ingest_test.py`) }}
@@ -76,7 +76,7 @@
     # feature_store_paths
     {{ skip (printf `%s/%s/%s` $root_dir $project_name_alphanumeric_underscore `feature_engineering`) }}
     {{ skip (printf `%s/%s/%s` $root_dir $project_name_alphanumeric_underscore `tests/feature_engineering`) }}
-    {{ skip (printf `%s/%s/%s` $root_dir $project_name_alphanumeric_underscore `training/notebooks/TrainWithFeatureStore.py`) }}
+    {{ skip (printf `%s/%s/%s` $root_dir $project_name_alphanumeric_underscore `training/TrainWithFeatureStore.py`) }}
     {{ skip (printf `%s/%s/%s` $root_dir $project_name_alphanumeric_underscore `resources/feature-engineering-workflow-resource.yml`) }}
 {{ end }}
 
diff --git a/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/README.md.tmpl b/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/README.md.tmpl
index cdb7f98e..b84e027a 100644
--- a/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/README.md.tmpl	
+++ b/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/README.md.tmpl	
@@ -146,7 +146,7 @@ In each module, there is `compute_features_fn` method that you need to implement
 The output dataframe will be persisted in a [time-series Feature Store table]({{ template `generate_doc_link` (map (pair "cloud" .input_cloud) (pair "path" "machine-learning/feature-store/time-series.html")) }}).
 See the example modules' documentation for more information.
 * Python unit tests for feature computation modules in `tests/feature_engineering` folder.
-* Feature engineering notebook, `feature_engineering/notebooks/GenerateAndWriteFeatures.py`, that reads input dataframes, dynamically loads feature computation modules, executes their `compute_features_fn` method and writes the outputs to a Feature Store table (creating it if missing).
+* Feature engineering notebook, `feature_engineering/GenerateAndWriteFeatures.py`, that reads input dataframes, dynamically loads feature computation modules, executes their `compute_features_fn` method and writes the outputs to a Feature Store table (creating it if missing).
 * Training notebook that [trains]({{ template `generate_doc_link` (map (pair "cloud" .input_cloud) (pair "path" "machine-learning/feature-store/train-models-with-feature-store.html")) }} ) a regression model by creating a training dataset using the Feature Store client.
 * Model deployment and batch inference notebooks that deploy and use the trained model. 
 * An automated integration test is provided (in `.github/workflows/{{ .input_project_name }}-run-tests.yml`) that executes a multi task run on Databricks involving the feature engineering and model training notebooks.
@@ -200,7 +200,7 @@ Otherwise, e.g. if iterating on ML code for a new project, follow the steps belo
 You can iterate on the feature transform modules locally in your favorite IDE before running them on Databricks.  
 
 #### Running code on Databricks
-You can iterate on ML code by running the provided `feature_engineering/notebooks/GenerateAndWriteFeatures.py` notebook on Databricks using
+You can iterate on ML code by running the provided `feature_engineering/GenerateAndWriteFeatures.py` notebook on Databricks using
 [Repos]({{ template `generate_doc_link` (map (pair "cloud" .input_cloud) (pair "path" "repos/index.html")) }}). This notebook drives execution of
 the feature transforms code defined under ``features``. You can use multiple browser tabs to edit
 logic in `features` and run the feature engineering pipeline in the `GenerateAndWriteFeatures.py` notebook.
diff --git a/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/deployment/batch_inference/notebooks/BatchInference.py.tmpl b/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/deployment/batch_inference/BatchInference.py.tmpl
similarity index 85%
rename from template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/deployment/batch_inference/notebooks/BatchInference.py.tmpl
rename to template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/deployment/batch_inference/BatchInference.py.tmpl
index 99bff2b9..8f893a6b 100644
--- a/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/deployment/batch_inference/notebooks/BatchInference.py.tmpl	
+++ b/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/deployment/batch_inference/BatchInference.py.tmpl	
@@ -39,14 +39,7 @@ dbutils.widgets.text(
 
 # COMMAND ----------
 
-import os
-
-notebook_path =  '/Workspace/' + os.path.dirname(dbutils.notebook.entry_point.getDbutils().notebook().getContext().notebookPath().get())
-%cd $notebook_path
-
-# COMMAND ----------
-
-# MAGIC %pip install -r ../../../requirements.txt
+# MAGIC %pip install -r ../../requirements.txt
 
 # COMMAND ----------
 
@@ -54,18 +47,7 @@ dbutils.library.restartPython()
 
 # COMMAND ----------
 
-import sys
-import os
-notebook_path =  '/Workspace/' + os.path.dirname(dbutils.notebook.entry_point.getDbutils().notebook().getContext().notebookPath().get())
-%cd $notebook_path
-%cd ..
-sys.path.append("../..")
-
-# COMMAND ----------
-
 # DBTITLE 1,Define input and output variables
-{{- if (eq .input_include_models_in_unity_catalog "no") }}
-from utils import get_deployed_model_stage_for_env{{end}}
 
 env = dbutils.widgets.get("env")
 input_table_name = dbutils.widgets.get("input_table_name")
@@ -75,7 +57,7 @@ assert input_table_name != "", "input_table_name notebook parameter must be spec
 assert output_table_name != "", "output_table_name notebook parameter must be specified"
 assert model_name != "", "model_name notebook parameter must be specified"
 {{- if (eq .input_include_models_in_unity_catalog "no") }}
-stage = get_deployed_model_stage_for_env(env)
+stage = {"dev": "Staging", "staging": "Staging", "prod": "Production", "test": "Production"}[env]
 model_uri = f"models:/{model_name}/{stage}"{{else}}
 alias = "champion"
 model_uri = f"models:/{model_name}@{alias}"{{end}}
diff --git a/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/deployment/batch_inference/predict.py.tmpl b/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/deployment/batch_inference/predict.py.tmpl
index 1e3c6283..f77c6cb9 100644
--- a/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/deployment/batch_inference/predict.py.tmpl	
+++ b/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/deployment/batch_inference/predict.py.tmpl	
@@ -13,39 +13,31 @@ def predict_batch(
     mlflow.set_registry_uri("databricks-uc")
     {{ end }}
     table = spark_session.table(input_table_name)
-    {{ if (eq .input_include_feature_store `yes`) }}     
-    {{ if (eq .input_include_models_in_unity_catalog `no`) }} 
-    from databricks.feature_store import FeatureStoreClient    
-    
-    fs_client = FeatureStoreClient()
-    
-    prediction_df = fs_client.score_batch(model_uri, table)
-    {{ else }}   
-    from databricks.feature_engineering import FeatureEngineeringClient    
-    
+    {{ if (eq .input_include_feature_store `yes`) }}
+    from databricks.feature_engineering import FeatureEngineeringClient
+
     fe_client = FeatureEngineeringClient()
-    
-    prediction_df = fe_client.score_batch(model_uri = model_uri, df = table)
-    {{ end }}
+
+    prediction_df = fe_client.score_batch(model_uri=model_uri, df=table)
     output_df = (
         prediction_df.withColumn("prediction", prediction_df["prediction"])
         .withColumn("model_id", lit(model_version))
         .withColumn("timestamp", to_timestamp(lit(ts)))
     )
-    {{ else }} 
+    {{ else }}
     predict = mlflow.pyfunc.spark_udf(
         spark_session, model_uri, result_type="double", env_manager="virtualenv"
-    )    
-    
+    )
+
     output_df = (
         table.withColumn("prediction", predict(struct(*table.columns)))
         .withColumn("model_id", lit(model_version))
         .withColumn("timestamp", to_timestamp(lit(ts)))
     )
     {{ end -}}
-    
+
     output_df.display()
 
     # Model predictions are written to the Delta table provided as input.
     # Delta is the default format in Databricks Runtime 8.0 and above.
-    output_df.write.format("delta").mode("overwrite").saveAsTable(output_table_name)
\ No newline at end of file
+    output_df.write.format("delta").mode("overwrite").saveAsTable(output_table_name)
diff --git a/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/deployment/model_deployment/notebooks/ModelDeployment.py.tmpl b/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/deployment/model_deployment/ModelDeployment.py.tmpl
similarity index 90%
rename from template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/deployment/model_deployment/notebooks/ModelDeployment.py.tmpl
rename to template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/deployment/model_deployment/ModelDeployment.py.tmpl
index 64f526ce..951ba945 100644
--- a/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/deployment/model_deployment/notebooks/ModelDeployment.py.tmpl	
+++ b/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/deployment/model_deployment/ModelDeployment.py.tmpl	
@@ -29,15 +29,6 @@ dbutils.widgets.dropdown("env", "None", ["None", "staging", "prod"], "Environmen
 
 # COMMAND ----------
 
-import os
-import sys
-notebook_path =  '/Workspace/' + os.path.dirname(dbutils.notebook.entry_point.getDbutils().notebook().getContext().notebookPath().get())
-%cd $notebook_path
-%cd ..
-sys.path.append("../..")
-
-# COMMAND ----------
-
 from deploy import deploy
 
 model_uri = dbutils.jobs.taskValues.get("Train", "model_uri", debugValue="")
diff --git a/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/deployment/model_deployment/deploy.py.tmpl b/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/deployment/model_deployment/deploy.py.tmpl
index f60c9617..c2677996 100644
--- a/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/deployment/model_deployment/deploy.py.tmpl	
+++ b/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/deployment/model_deployment/deploy.py.tmpl	
@@ -1,8 +1,4 @@
 import sys
-import pathlib
-
-sys.path.append(str(pathlib.Path(__file__).parent.parent.parent.resolve()))
-{{if (eq .input_include_models_in_unity_catalog "no")}}from utils import get_deployed_model_stage_for_env{{end}}
 from mlflow.tracking import MlflowClient
 
 {{ if (eq .input_include_models_in_unity_catalog "no") }}
@@ -19,7 +15,7 @@ def deploy(model_uri, env):
     _, model_name, version = model_uri.split("/")
     client = MlflowClient()
     mv = client.get_model_version(model_name, version)
-    target_stage = get_deployed_model_stage_for_env(env)
+    target_stage = {"dev": "Staging", "staging": "Staging", "prod": "Production", "test": "Production"}[env]
     if mv.current_stage != target_stage:
         client.transition_model_version_stage(
             name=model_name,
diff --git a/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/feature_engineering/notebooks/GenerateAndWriteFeatures.py.tmpl b/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/feature_engineering/GenerateAndWriteFeatures.py.tmpl
similarity index 84%
rename from template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/feature_engineering/notebooks/GenerateAndWriteFeatures.py.tmpl
rename to template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/feature_engineering/GenerateAndWriteFeatures.py.tmpl
index c8ac612c..1a718769 100644
--- a/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/feature_engineering/notebooks/GenerateAndWriteFeatures.py.tmpl	
+++ b/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/feature_engineering/GenerateAndWriteFeatures.py.tmpl	
@@ -63,13 +63,6 @@ dbutils.widgets.text(
 
 # COMMAND ----------
 
-import os
-notebook_path =  '/Workspace/' + os.path.dirname(dbutils.notebook.entry_point.getDbutils().notebook().getContext().notebookPath().get())
-%cd $notebook_path
-%cd ../features
-
-# COMMAND ----------
-
 # DBTITLE 1,Define input and output variables
 input_table_path = dbutils.widgets.get("input_table_path")
 output_table_name = dbutils.widgets.get("output_table_name")
@@ -101,7 +94,7 @@ raw_data = spark.read.format("delta").load(input_table_path)
 # Compute the features. This is done by dynamically loading the features module.
 from importlib import import_module
 
-mod = import_module(features_module)
+mod = import_module(f"features.{features_module}")
 compute_features_fn = getattr(mod, "compute_features_fn")
 
 features_df = compute_features_fn(
@@ -114,26 +107,6 @@ features_df = compute_features_fn(
 # COMMAND ----------
 
 # DBTITLE 1, Write computed features.
-{{- if (eq .input_include_models_in_unity_catalog `no`) }}
-from databricks import feature_store
-
-fs = feature_store.FeatureStoreClient()
-
-# Create the feature table if it does not exist first.
-# Note that this is a no-op if a table with the same name and schema already exists.
-fs.create_table(
-    name=output_table_name,
-    primary_keys=[x.strip() for x in pk_columns.split(",")],
-    timestamp_keys=[ts_column],
-    df=features_df,
-)
-
-# Write the computed features dataframe.
-fs.write_table(
-    name=output_table_name,
-    df=features_df,
-    mode="merge",
-){{ else }}
 from databricks.feature_engineering import FeatureEngineeringClient
 
 fe = FeatureEngineeringClient()
@@ -141,8 +114,8 @@ fe = FeatureEngineeringClient()
 # Create the feature table if it does not exist first.
 # Note that this is a no-op if a table with the same name and schema already exists.
 fe.create_table(
-    name=output_table_name,    
-    primary_keys=[x.strip() for x in pk_columns.split(",")] + [ts_column],  # Include timeseries column in primary_keys
+    name=output_table_name,
+    primary_keys=[x.strip() for x in pk_columns.split(",")] + [ts_column],
     timestamp_keys=[ts_column],
     df=features_df,
 )
@@ -152,7 +125,7 @@ fe.write_table(
     name=output_table_name,
     df=features_df,
     mode="merge",
-){{ end }}
+)
 
 # COMMAND ----------
 
diff --git a/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/monitoring/notebooks/MonitoredMetricViolationCheck.py.tmpl b/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/monitoring/MonitoredMetricViolationCheck.py.tmpl
similarity index 91%
rename from template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/monitoring/notebooks/MonitoredMetricViolationCheck.py.tmpl
rename to template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/monitoring/MonitoredMetricViolationCheck.py.tmpl
index bfec9085..7db01bf1 100644
--- a/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/monitoring/notebooks/MonitoredMetricViolationCheck.py.tmpl	
+++ b/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/monitoring/MonitoredMetricViolationCheck.py.tmpl	
@@ -40,15 +40,6 @@ dbutils.widgets.text(
 
 # COMMAND ----------
 
-import os
-import sys
-notebook_path =  '/Workspace/' + os.path.dirname(dbutils.notebook.entry_point.getDbutils().notebook().getContext().notebookPath().get())
-%cd $notebook_path
-%cd ..
-sys.path.append("../..")
-
-# COMMAND ----------
-
 from metric_violation_check_query import sql_query
 
 table_name_under_monitor = dbutils.widgets.get("table_name_under_monitor")
diff --git a/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/monitoring/metric_violation_check_query.py.tmpl b/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/monitoring/metric_violation_check_query.py.tmpl
index 1dc40225..7be5eb29 100644
--- a/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/monitoring/metric_violation_check_query.py.tmpl	
+++ b/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/monitoring/metric_violation_check_query.py.tmpl	
@@ -1,10 +1,5 @@
 # This file is used for the main SQL query that checks the last {num_evaluation_windows} metric violations and whether at least {num_violation_windows} of those runs violate the condition.
 
-import sys
-import pathlib
-
-sys.path.append(str(pathlib.Path(__file__).parent.parent.parent.resolve()))
-
 """The SQL query is divided into three main parts. The first part selects the top {num_evaluation_windows}
 values of the metric to be monitored, ordered by the time window, and saves as recent_metrics.
 ```sql
diff --git a/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/resources/README.md.tmpl b/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/resources/README.md.tmpl
index a76b3ec0..231b1b44 100644
--- a/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/resources/README.md.tmpl	
+++ b/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/resources/README.md.tmpl	
@@ -163,7 +163,7 @@ Its central purpose is to evaluate a registered model and validate its quality b
 Model validation contains three components: 
 * [model-workflow-resource.yml](./model-workflow-resource.yml) contains the resource config and input parameters for model validation.
 * [validation.py](../validation/validation.py) defines custom metrics and validation thresholds that are referenced by the above resource config files.
-* [notebooks/ModelValidation](../validation/notebooks/ModelValidation.py) contains the validation job implementation. In most cases you don't need to modify this file.
+* [ModelValidation](../validation/ModelValidation.py) contains the validation job implementation. In most cases you don't need to modify this file.
 
 To set up and enable model validation, update [validation.py](../validation/validation.py) to return desired custom metrics and validation thresholds, then 
 resolve the `TODOs` in the ModelValidation task of [model-workflow-resource.yml](./model-workflow-resource.yml).
@@ -177,9 +177,9 @@ Its central purpose is to track production model performances, feature distribut
 
 Monitoring contains four components:
 * [metric_violation_check_query.py](../monitoring/metric_violation_check_query.py) defines a query that checks for violation of the monitored metric.
-* [notebooks/MonitoredMetricViolationCheck](../monitoring/notebooks/MonitoredMetricViolationCheck.py) acts as an entry point, executing the violation check query against the monitored inference table.
+* [MonitoredMetricViolationCheck](../monitoring/MonitoredMetricViolationCheck.py) acts as an entry point, executing the violation check query against the monitored inference table.
 It emits a boolean value based on the query result.
-* [monitoring-resource.yml](./monitoring-resource.yml) contains the resource config, inputs parameters for monitoring, and orchestrates model retraining based on monitoring. It first runs the [notebooks/MonitoredMetricViolationCheck](../monitoring/notebooks/MonitoredMetricViolationCheck.py)
+* [monitoring-resource.yml](./monitoring-resource.yml) contains the resource config, inputs parameters for monitoring, and orchestrates model retraining based on monitoring. It first runs the [MonitoredMetricViolationCheck](../monitoring/MonitoredMetricViolationCheck.py)
 entry point then decides whether to execute the model retraining workflow.
 
 To set up and enable monitoring:
@@ -226,7 +226,7 @@ resources:
         - task_key: batch_inference_job
           <<: *new_cluster
           notebook_task:
-            notebook_path: ../deployment/batch_inference/notebooks/BatchInference.py
+            notebook_path: ../deployment/batch_inference/BatchInference.py
             base_parameters:
               env: ${bundle.target}
               input_table_name: batch_inference_input_table_name
@@ -234,12 +234,12 @@ resources:
 ```
 
 The example above defines a Databricks job with name `${bundle.target}-{{ .input_project_name }}-batch-inference-job`
-that runs the notebook under `{{template `project_name_alphanumeric_underscore` .}}/deployment/batch_inference/notebooks/BatchInference.py` to regularly apply your ML model for batch inference. 
+that runs the notebook under `{{template `project_name_alphanumeric_underscore` .}}/deployment/batch_inference/BatchInference.py` to regularly apply your ML model for batch inference. 
 
 At the start of the resource definition, we declared an anchor `new_cluster` that will be referenced and used later. For more information about anchors in yaml schema, please refer to the [yaml documentation](https://yaml.org/spec/1.2.2/#3222-anchors-and-aliases).
 
 We specify a `batch_inference_job` under `resources/jobs` to define a databricks workflow with internal key `batch_inference_job` and job name `{bundle.target}-{{ .input_project_name }}-batch-inference-job`.
-The workflow contains a single task with task key `batch_inference_job`. The task runs notebook `../deployment/batch_inference/notebooks/BatchInference.py` with provided parameters `env` and `input_table_name` passing to the notebook.
+The workflow contains a single task with task key `batch_inference_job`. The task runs notebook `../deployment/batch_inference/BatchInference.py` with provided parameters `env` and `input_table_name` passing to the notebook.
 After setting up databricks CLI, you can run command `databricks bundle schema`  to learn more about databricks CLI bundles schema.
 
 The notebook_path is the relative path starting from the resource yaml file.
@@ -281,7 +281,7 @@ resources:
         - task_key: batch_inference_job
           <<: *new_cluster
           notebook_task:
-            notebook_path: ../deployment/batch_inference/notebooks/BatchInference.py
+            notebook_path: ../deployment/batch_inference/BatchInference.py
             base_parameters:
               env: ${bundle.target}
               input_table_name: ${var.batch_inference_input_table}
diff --git a/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/resources/batch-inference-workflow-resource.yml.tmpl b/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/resources/batch-inference-workflow-resource.yml.tmpl
index e11b6306..74b9fdaa 100644
--- a/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/resources/batch-inference-workflow-resource.yml.tmpl	
+++ b/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/resources/batch-inference-workflow-resource.yml.tmpl	
@@ -20,7 +20,7 @@ resources:
         - task_key: batch_inference_job
           <<: *new_cluster
           notebook_task:
-            notebook_path: ../deployment/batch_inference/notebooks/BatchInference.py
+            notebook_path: ../deployment/batch_inference/BatchInference.py
             base_parameters:
               env: ${bundle.target}
               {{ if (eq .input_include_feature_store `yes`) }}{{ if (eq .input_include_models_in_unity_catalog `yes`) }}input_table_name: ${bundle.target}.{{ .input_schema_name }}.feature_store_inference_input  # TODO: create input table for inference
diff --git a/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/resources/feature-engineering-workflow-resource.yml.tmpl b/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/resources/feature-engineering-workflow-resource.yml.tmpl
index 8818181e..fc9d8df4 100644
--- a/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/resources/feature-engineering-workflow-resource.yml.tmpl	
+++ b/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/resources/feature-engineering-workflow-resource.yml.tmpl	
@@ -23,7 +23,7 @@ resources:
         - task_key: PickupFeatures
           job_cluster_key: write_feature_table_job_cluster
           notebook_task:
-            notebook_path: ../feature_engineering/notebooks/GenerateAndWriteFeatures.py
+            notebook_path: ../feature_engineering/GenerateAndWriteFeatures.py
             base_parameters:
               # TODO modify these arguments to reflect your setup.
               input_table_path: /databricks-datasets/nyctaxi-with-zipcodes/subsampled
@@ -40,7 +40,7 @@ resources:
         - task_key: DropoffFeatures
           job_cluster_key: write_feature_table_job_cluster
           notebook_task:
-            notebook_path: ../feature_engineering/notebooks/GenerateAndWriteFeatures.py
+            notebook_path: ../feature_engineering/GenerateAndWriteFeatures.py
             base_parameters:
               # TODO: modify these arguments to reflect your setup.
               input_table_path: /databricks-datasets/nyctaxi-with-zipcodes/subsampled
diff --git a/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/resources/model-workflow-resource.yml.tmpl b/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/resources/model-workflow-resource.yml.tmpl
index 3c063b75..84deaebd 100644
--- a/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/resources/model-workflow-resource.yml.tmpl	
+++ b/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/resources/model-workflow-resource.yml.tmpl	
@@ -23,7 +23,7 @@ resources:
         - task_key: Train
           job_cluster_key: model_training_job_cluster
           {{ if and (eq .input_include_feature_store `no`) (eq .input_include_mlflow_recipes `no`) }}notebook_task:
-            notebook_path: ../training/notebooks/Train.py
+            notebook_path: ../training/Train.py
             base_parameters:
               env: ${bundle.target}
               # TODO: Update training_data_path
@@ -34,7 +34,7 @@ resources:
               # git source information of current ML resource deployment. It will be persisted as part of the workflow run
               git_source_info: url:${bundle.git.origin_url}; branch:${bundle.git.branch}; commit:${bundle.git.commit}
           {{ else if (eq .input_include_feature_store `yes`) }}notebook_task:
-            notebook_path: ../training/notebooks/TrainWithFeatureStore.py
+            notebook_path: ../training/TrainWithFeatureStore.py
             base_parameters:
               env: ${bundle.target}
               # TODO: Update training_data_path
@@ -49,7 +49,7 @@ resources:
               # git source information of current ML resource deployment. It will be persisted as part of the workflow run
               git_source_info: url:${bundle.git.origin_url}; branch:${bundle.git.branch}; commit:${bundle.git.commit}
           {{- else -}}notebook_task:
-            notebook_path: ../training/notebooks/TrainWithMLflowRecipes.py
+            notebook_path: ../training/TrainWithMLflowRecipes.py
             base_parameters:
               env: ${bundle.target}
               # git source information of current ML resource deployment. It will be persisted as part of the workflow run
@@ -59,7 +59,7 @@ resources:
           depends_on:
             - task_key: Train
           notebook_task:
-            notebook_path: ../validation/notebooks/ModelValidation.py
+            notebook_path: ../validation/ModelValidation.py
             base_parameters:
               {{- if (eq .input_include_mlflow_recipes `yes`) }}
               env: ${bundle.target}{{ end }}
@@ -105,7 +105,7 @@ resources:
           depends_on:
             - task_key: ModelValidation
           notebook_task:
-            notebook_path: ../deployment/model_deployment/notebooks/ModelDeployment.py
+            notebook_path: ../deployment/model_deployment/ModelDeployment.py
             base_parameters:
               env: ${bundle.target}
               # git source information of current ML resource deployment. It will be persisted as part of the workflow run
diff --git a/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/resources/monitoring-resource.yml.tmpl b/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/resources/monitoring-resource.yml.tmpl
index c68d64c7..de14e471 100644
--- a/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/resources/monitoring-resource.yml.tmpl	
+++ b/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/resources/monitoring-resource.yml.tmpl	
@@ -41,7 +41,7 @@ resources:
         - task_key: monitored_metric_violation_check
           <<: *new_cluster
           notebook_task:
-            notebook_path: ../monitoring/notebooks/MonitoredMetricViolationCheck.py
+            notebook_path: ../monitoring/MonitoredMetricViolationCheck.py
             base_parameters:
               env: ${bundle.target}
               table_name_under_monitor: {{ .input_inference_table_name }}
diff --git a/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/training/README.md.tmpl b/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/training/README.md.tmpl
index efc2e449..ed004fec 100644
--- a/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/training/README.md.tmpl	
+++ b/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/training/README.md.tmpl	
@@ -72,7 +72,7 @@ Otherwise, e.g. if iterating on ML code for a new project, follow the steps belo
   `dbx sync repo --profile {{ .input_project_name }}-dev --source . --dest-repo your-repo-name`, where `your-repo-name` should be the last segment of the full repo name (`/Repos/username/your-repo-name`)
 
 #### Running code on Databricks
-You can iterate on ML code by running the provided `{{template `project_name_alphanumeric_underscore` .}}/training/notebooks/TrainWithMLflowRecipes.py` notebook on Databricks using
+You can iterate on ML code by running the provided `{{template `project_name_alphanumeric_underscore` .}}/training/TrainWithMLflowRecipes.py` notebook on Databricks using
 [Repos]({{ template `generate_doc_link` (map (pair "cloud" .input_cloud) (pair "path" "repos/index.html")) }}). This notebook drives execution of
 the ML code defined under ``{{template `project_name_alphanumeric_underscore` .}}/training/steps``. You can use multiple browser tabs to edit
 logic in `steps` and run the training recipe in the `TrainWithMLflowRecipes.py` notebook.
diff --git a/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/training/notebooks/Train.py.tmpl b/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/training/Train.py.tmpl
similarity index 95%
rename from template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/training/notebooks/Train.py.tmpl
rename to template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/training/Train.py.tmpl
index eb909157..3286e277 100644
--- a/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/training/notebooks/Train.py.tmpl	
+++ b/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/training/Train.py.tmpl	
@@ -26,13 +26,7 @@
 
 # COMMAND ----------
 
-import os
-notebook_path =  '/Workspace/' + os.path.dirname(dbutils.notebook.entry_point.getDbutils().notebook().getContext().notebookPath().get())
-%cd $notebook_path
-
-# COMMAND ----------
-
-# MAGIC %pip install -r ../../requirements.txt
+# MAGIC %pip install -r ../requirements.txt
 
 # COMMAND ----------
 
diff --git a/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/training/notebooks/TrainWithFeatureStore.py.tmpl b/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/training/TrainWithFeatureStore.py.tmpl
similarity index 77%
rename from template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/training/notebooks/TrainWithFeatureStore.py.tmpl
rename to template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/training/TrainWithFeatureStore.py.tmpl
index aa2e35f3..923cad9f 100644
--- a/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/training/notebooks/TrainWithFeatureStore.py.tmpl	
+++ b/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/training/TrainWithFeatureStore.py.tmpl	
@@ -26,13 +26,7 @@
 
 # COMMAND ----------
 
-import os
-notebook_path =  '/Workspace/' + os.path.dirname(dbutils.notebook.entry_point.getDbutils().notebook().getContext().notebookPath().get())
-%cd $notebook_path
-
-# COMMAND ----------
-
-# MAGIC %pip install -r ../../requirements.txt
+# MAGIC %pip install -r ../requirements.txt
 
 # COMMAND ----------
 
@@ -177,33 +171,6 @@ taxi_data.display()
 # COMMAND ----------
 
 # DBTITLE 1, Create FeatureLookups
-{{- if (eq .input_include_models_in_unity_catalog `no`) }}
-from databricks.feature_store import FeatureLookup
-import mlflow
-
-pickup_features_table = dbutils.widgets.get("pickup_features_table")
-dropoff_features_table = dbutils.widgets.get("dropoff_features_table")
-
-pickup_feature_lookups = [
-    FeatureLookup(
-        table_name=pickup_features_table,
-        feature_names=[
-            "mean_fare_window_1h_pickup_zip",
-            "count_trips_window_1h_pickup_zip",
-        ],
-        lookup_key=["pickup_zip"],
-        timestamp_lookup_key=["rounded_pickup_datetime"],
-    ),
-]
-
-dropoff_feature_lookups = [
-    FeatureLookup(
-        table_name=dropoff_features_table,
-        feature_names=["count_trips_window_30m_dropoff_zip", "dropoff_is_weekend"],
-        lookup_key=["dropoff_zip"],
-        timestamp_lookup_key=["rounded_dropoff_datetime"],
-    ),
-]{{ else }}
 from databricks.feature_engineering import FeatureLookup
 import mlflow
 
@@ -229,34 +196,11 @@ dropoff_feature_lookups = [
         lookup_key=["dropoff_zip"],
         timestamp_lookup_key=["rounded_dropoff_datetime"],
     ),
-]{{ end }}
+]
 
 # COMMAND ----------
 
 # DBTITLE 1, Create Training Dataset
-{{ if (eq .input_include_models_in_unity_catalog `no`) }}
-from databricks import feature_store
-
-# End any existing runs (in the case this notebook is being run for a second time)
-mlflow.end_run()
-
-# Start an mlflow run, which is needed for the feature store to log the model
-mlflow.start_run()
-
-# Since the rounded timestamp columns would likely cause the model to overfit the data
-# unless additional feature engineering was performed, exclude them to avoid training on them.
-exclude_columns = ["rounded_pickup_datetime", "rounded_dropoff_datetime"]
-
-fs = feature_store.FeatureStoreClient()
-
-# Create the training set that includes the raw input data merged with corresponding features from both feature tables
-training_set = fs.create_training_set(
-    taxi_data,
-    feature_lookups=pickup_feature_lookups + dropoff_feature_lookups,
-    label="fare_amount",
-    exclude_columns=exclude_columns,
-)
-{{ else }}
 from databricks.feature_engineering import FeatureEngineeringClient
 
 # End any existing runs (in the case this notebook is being run for a second time)
@@ -273,13 +217,11 @@ fe = FeatureEngineeringClient()
 
 # Create the training set that includes the raw input data merged with corresponding features from both feature tables
 training_set = fe.create_training_set(
-    df=taxi_data, # specify the df 
-    feature_lookups=pickup_feature_lookups + dropoff_feature_lookups, 
-    # both features need to be available; defined in GenerateAndWriteFeatures &/or feature-engineering-workflow-resource.yml
+    df=taxi_data,
+    feature_lookups=pickup_feature_lookups + dropoff_feature_lookups,
     label="fare_amount",
     exclude_columns=exclude_columns,
 )
-{{ end }}
 
 # Load the TrainingSet into a dataframe which can be passed into sklearn for training a model
 training_df = training_set.load_df()
@@ -292,7 +234,7 @@ training_df.display()
 # COMMAND ----------
 
 # MAGIC %md
-# MAGIC Train a LightGBM model on the data returned by `TrainingSet.to_df`, then log the model with `FeatureStoreClient.log_model`. The model will be packaged with feature metadata.
+# MAGIC Train a LightGBM model on the data returned by `TrainingSet.to_df`, then log the model with `FeatureEngineeringClient.log_model`. The model will be packaged with feature metadata.
 
 # COMMAND ----------
 
@@ -327,24 +269,15 @@ model = lgb.train(param, train_lgb_dataset, num_rounds)
 # COMMAND ----------
 
 # DBTITLE 1, Log model and return output.
-{{- if (eq .input_include_models_in_unity_catalog `no`) }}
-# Log the trained model with MLflow and package it with feature lookup information.
-fs.log_model(
-    model=model, #specify model
-    artifact_path="model_packaged",
-    flavor=mlflow.lightgbm,
-    training_set=training_set,
-    registered_model_name=model_name,
-){{ else }}
+
 # Log the trained model with MLflow and package it with feature lookup information.
 fe.log_model(
-    model=model, #specify model
+    model=model,
     artifact_path="model_packaged",
     flavor=mlflow.lightgbm,
     training_set=training_set,
     registered_model_name=model_name,
 )
-{{ end }}
 
 # The returned model URI is needed by the model deployment notebook.
 model_version = get_latest_model_version(model_name)
diff --git a/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/training/notebooks/TrainWithMLflowRecipes.py.tmpl b/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/training/TrainWithMLflowRecipes.py.tmpl
similarity index 91%
rename from template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/training/notebooks/TrainWithMLflowRecipes.py.tmpl
rename to template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/training/TrainWithMLflowRecipes.py.tmpl
index c06cc6c4..b2a9069f 100644
--- a/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/training/notebooks/TrainWithMLflowRecipes.py.tmpl	
+++ b/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/training/TrainWithMLflowRecipes.py.tmpl	
@@ -42,13 +42,7 @@
 
 # COMMAND ----------
 
-import os
-notebook_path =  '/Workspace/' + os.path.dirname(dbutils.notebook.entry_point.getDbutils().notebook().getContext().notebookPath().get())
-%cd $notebook_path
-
-# COMMAND ----------
-
-# MAGIC %pip install -r ../../requirements.txt
+# MAGIC %pip install -r ../requirements.txt
 
 # COMMAND ----------
 
@@ -56,13 +50,6 @@ dbutils.library.restartPython()
 
 # COMMAND ----------
 
-import os
-notebook_path =  '/Workspace/' + os.path.dirname(dbutils.notebook.entry_point.getDbutils().notebook().getContext().notebookPath().get())
-%cd $notebook_path
-%cd ../
-
-# COMMAND ----------
-
 from mlflow.recipes import Recipe
 
 try:
diff --git a/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/validation/notebooks/ModelValidation.py.tmpl b/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/validation/ModelValidation.py.tmpl
similarity index 96%
rename from template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/validation/notebooks/ModelValidation.py.tmpl
rename to template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/validation/ModelValidation.py.tmpl
index c2852616..876461ca 100644
--- a/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/validation/notebooks/ModelValidation.py.tmpl	
+++ b/template/{{.input_root_dir}}/{{template `project_name_alphanumeric_underscore` .}}/validation/ModelValidation.py.tmpl	
@@ -43,13 +43,7 @@
 
 # COMMAND ----------
 
-import os
-notebook_path =  '/Workspace/' + os.path.dirname(dbutils.notebook.entry_point.getDbutils().notebook().getContext().notebookPath().get())
-%cd $notebook_path
-
-# COMMAND ----------
-
-# MAGIC %pip install -r ../../requirements.txt
+# MAGIC %pip install -r ../requirements.txt
 
 # COMMAND ----------
 
@@ -57,13 +51,6 @@ dbutils.library.restartPython()
 
 # COMMAND ----------
 
-import os
-notebook_path =  '/Workspace/' + os.path.dirname(dbutils.notebook.entry_point.getDbutils().notebook().getContext().notebookPath().get())
-%cd $notebook_path
-%cd ../
-
-# COMMAND ----------
-
 {{ if (eq .input_include_mlflow_recipes `yes`) }}dbutils.widgets.dropdown(
     "env", "prod", ["staging", "prod"], "Environment(for input data)"
 ){{ end -}}
@@ -316,12 +303,12 @@ data = rounded_taxi_data(data)
 # MLflow evaluate can take a lambda function instead of a model uri for a model
 # but id does not work for the baseline model as it requires a model_uri (baseline comparison is set to false)
 
-from databricks.feature_store import FeatureStoreClient
+from databricks.feature_engineering import FeatureEngineeringClient
 
 def get_fs_model(df):
-    fs_client = FeatureStoreClient()
+    fe_client = FeatureEngineeringClient()
     return (
-        fs_client.score_batch(model_uri, spark.createDataFrame(df))
+        fe_client.score_batch(model_uri=model_uri, df=spark.createDataFrame(df))
         .select("prediction")
         .toPandas()
     )