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diff --git a/README.md b/README.md
@@ -12,7 +12,7 @@ Get started now!
 
 ## What's New
 
-- Feb 2025: Added a post evaluation notebook that shows how to run fine-grained model selection after running MMF. Try the [notebook](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/post-evaluation-analysis.py).
+- Feb 2025: Added a post evaluation notebook that shows how to run fine-grained model selection after running MMF. Try the [notebook](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/post-evaluation-analysis.ipynb).
 - Jan 2025: [TimesFM](https://github.com/google-research/timesfm) is available for univariate and covariate forecasting. Try the notebooks: [univariate](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/daily/foundation_daily.py) and [covariate](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/external_regressors/foundation_external_regressors_daily.py).
 - Jan 2025: [Chronos Bolt](https://github.com/amazon-science/chronos-forecasting) models are available for univariate forecasting. Try the [notebook](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/daily/foundation_daily.py).
 - Jan 2025: [Moirai MoE](https://github.com/SalesforceAIResearch/uni2ts) models are available for univariate forecasting. Try the [notebook](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/daily/foundation_daily.py).
@@ -111,7 +111,7 @@ run_forecast(
 
 To modify the model hyperparameters, change the values in [mmf_sa/models/models_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/models/models_conf.yaml) or overwrite these values in [mmf_sa/forecasting_conf.yaml](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/mmf_sa/forecasting_conf.yaml). 
 
-MMF is fully integrated with MLflow, so once the training kicks off, the experiments will be visible in the MLflow Tracking UI with the corresponding metrics and parameters (note that we do not log all local models in MLFlow, but we store the binaries in the tables ```evaluation_output``` and ```scoring_output```). The metric you see in the MLflow Tracking UI is a simple mean over backtesting trials over all time series. Refer to the [notebook](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/post-evaluation-analysis.py) for guidance on performing fine-grained model selection after running `run_forecast`.
+MMF is fully integrated with MLflow, so once the training kicks off, the experiments will be visible in the MLflow Tracking UI with the corresponding metrics and parameters (note that we do not log all local models in MLFlow, but we store the binaries in the tables ```evaluation_output``` and ```scoring_output```). The metric you see in the MLflow Tracking UI is a simple mean over backtesting trials over all time series. Refer to the [notebook](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/post-evaluation-analysis.ipynb) for guidance on performing fine-grained model selection after running `run_forecast`.
 
 We encourage you to read through [examples/daily/local_univariate_daily.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/daily/local_univariate_daily.py) notebook to better understand how local models can be applied to your time series using MMF. An example notebook for forecasting with exogenous regressors can be found in [examples/external_regressors/local_univariate_external_regressors_daily.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/external_regressors/local_univariate_external_regressors_daily.py).
 
@@ -190,7 +190,7 @@ To modify the model hyperparameters or reset the range of the hyperparameter sea
 
 MMF is fully integrated with MLflow and so once the training kicks off, the experiments will be visible in the MLflow Tracking UI with the corresponding metrics and parameters. Once the training is complete the models will be logged to MLFlow and registered to Unity Catalog. 
 
-We encourage you to read through [examples/daily/global_daily.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/daily/global_daily.py) notebook to better understand how global models can be applied to your time series using MMF. An example notebook for forecasting with exogenous regressors can be found in [examples/external_regressors/global_external_regressors_daily.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/external_regressors/global_external_regressors_daily.py). Refer to the [notebook](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/post-evaluation-analysis.py) for guidance on performing fine-grained model selection after running `run_forecast`.
+We encourage you to read through [examples/daily/global_daily.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/daily/global_daily.py) notebook to better understand how global models can be applied to your time series using MMF. An example notebook for forecasting with exogenous regressors can be found in [examples/external_regressors/global_external_regressors_daily.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/external_regressors/global_external_regressors_daily.py). Refer to the [notebook](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/post-evaluation-analysis.ipynb) for guidance on performing fine-grained model selection after running `run_forecast`.
 
 ### Foundation Models
 
@@ -239,7 +239,7 @@ To modify the model hyperparameters, change the values in [mmf_sa/models/models_
 
 MMF is fully integrated with MLflow and so once the training kicks off, the experiments will be visible in the MLflow Tracking UI with the corresponding metrics and parameters. During the evaluation, the models are logged and registered to Unity Catalog.
 
-We encourage you to read through [examples/daily/foundation_daily.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/daily/foundation_daily.py) notebook to better understand how foundation models can be applied to your time series using MMF. An example notebook for forecasting with exogenous regressors can be found in [examples/external_regressors/foundation_external_regressors_daily.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/external_regressors/foundation_external_regressors_daily.py). Refer to the [notebook](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/post-evaluation-analysis.py) for guidance on performing fine-grained model selection after running `run_forecast`.
+We encourage you to read through [examples/daily/foundation_daily.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/daily/foundation_daily.py) notebook to better understand how foundation models can be applied to your time series using MMF. An example notebook for forecasting with exogenous regressors can be found in [examples/external_regressors/foundation_external_regressors_daily.py](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/external_regressors/foundation_external_regressors_daily.py). Refer to the [notebook](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/post-evaluation-analysis.ipynb) for guidance on performing fine-grained model selection after running `run_forecast`.
 
 #### Using Time Series Foundation Models on Databricks
 

diff --git a/examples/daily/foundation_daily.py b/examples/daily/foundation_daily.py
@@ -201,7 +201,7 @@ def transform_group(df):
 # COMMAND ----------
 
 # MAGIC %md
-# MAGIC Refer to the [notebook](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/post-evaluation-analysis.py) for guidance on performing fine-grained model selection after running `run_forecast`.
+# MAGIC Refer to the [notebook](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/post-evaluation-analysis.ipynb) for guidance on performing fine-grained model selection after running `run_forecast`.
 
 # COMMAND ----------
 

diff --git a/examples/daily/global_daily.py b/examples/daily/global_daily.py
@@ -194,7 +194,7 @@ def transform_group(df):
 # COMMAND ----------
 
 # MAGIC %md
-# MAGIC Refer to the [notebook](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/post-evaluation-analysis.py) for guidance on performing fine-grained model selection after running `run_forecast`.
+# MAGIC Refer to the [notebook](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/post-evaluation-analysis.ipynb) for guidance on performing fine-grained model selection after running `run_forecast`.
 
 # COMMAND ----------
 

diff --git a/examples/daily/local_univariate_daily.py b/examples/daily/local_univariate_daily.py
@@ -232,7 +232,7 @@ def transform_group(df):
 # COMMAND ----------
 
 # MAGIC %md
-# MAGIC Refer to the [notebook](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/post-evaluation-analysis.py) for guidance on performing fine-grained model selection after running `run_forecast`.
+# MAGIC Refer to the [notebook](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/post-evaluation-analysis.ipynb) for guidance on performing fine-grained model selection after running `run_forecast`.
 
 # COMMAND ----------
 

diff --git a/examples/external_regressors/foundation_external_regressors_daily.py b/examples/external_regressors/foundation_external_regressors_daily.py
@@ -153,7 +153,7 @@
 # COMMAND ----------
 
 # MAGIC %md
-# MAGIC Refer to the [notebook](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/post-evaluation-analysis.py) for guidance on performing fine-grained model selection after running `run_forecast`.
+# MAGIC Refer to the [notebook](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/post-evaluation-analysis.ipynb) for guidance on performing fine-grained model selection after running `run_forecast`.
 
 # COMMAND ----------
 

diff --git a/examples/external_regressors/global_external_regressors_daily.py b/examples/external_regressors/global_external_regressors_daily.py
@@ -160,7 +160,7 @@
 # COMMAND ----------
 
 # MAGIC %md
-# MAGIC Refer to the [notebook](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/post-evaluation-analysis.py) for guidance on performing fine-grained model selection after running `run_forecast`.
+# MAGIC Refer to the [notebook](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/post-evaluation-analysis.ipynb) for guidance on performing fine-grained model selection after running `run_forecast`.
 
 # COMMAND ----------
 

diff --git a/examples/external_regressors/local_univariate_external_regressors_daily.py b/examples/external_regressors/local_univariate_external_regressors_daily.py
@@ -191,7 +191,7 @@
 # COMMAND ----------
 
 # MAGIC %md
-# MAGIC Refer to the [notebook](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/post-evaluation-analysis.py) for guidance on performing fine-grained model selection after running `run_forecast`.
+# MAGIC Refer to the [notebook](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/post-evaluation-analysis.ipynb) for guidance on performing fine-grained model selection after running `run_forecast`.
 
 # COMMAND ----------
 

diff --git a/examples/hourly/foundation_hourly.py b/examples/hourly/foundation_hourly.py
@@ -181,7 +181,7 @@ def transform_group(df):
 # COMMAND ----------
 
 # MAGIC %md
-# MAGIC Refer to the [notebook](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/post-evaluation-analysis.py) for guidance on performing fine-grained model selection after running `run_forecast`.
+# MAGIC Refer to the [notebook](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/post-evaluation-analysis.ipynb) for guidance on performing fine-grained model selection after running `run_forecast`.
 
 # COMMAND ----------
 

diff --git a/examples/hourly/global_hourly.py b/examples/hourly/global_hourly.py
@@ -175,7 +175,7 @@ def transform_group(df):
 # COMMAND ----------
 
 # MAGIC %md
-# MAGIC Refer to the [notebook](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/post-evaluation-analysis.py) for guidance on performing fine-grained model selection after running `run_forecast`.
+# MAGIC Refer to the [notebook](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/post-evaluation-analysis.ipynb) for guidance on performing fine-grained model selection after running `run_forecast`.
 
 # COMMAND ----------
 

diff --git a/examples/hourly/local_univariate_hourly.py b/examples/hourly/local_univariate_hourly.py
@@ -202,7 +202,7 @@ def transform_group(df):
 # COMMAND ----------
 
 # MAGIC %md
-# MAGIC Refer to the [notebook](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/post-evaluation-analysis.py) for guidance on performing fine-grained model selection after running `run_forecast`.
+# MAGIC Refer to the [notebook](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/post-evaluation-analysis.ipynb) for guidance on performing fine-grained model selection after running `run_forecast`.
 
 # COMMAND ----------
 

diff --git a/examples/monthly/foundation_monthly.py b/examples/monthly/foundation_monthly.py
@@ -190,7 +190,7 @@ def transform_group(df):
 # COMMAND ----------
 
 # MAGIC %md
-# MAGIC Refer to the [notebook](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/post-evaluation-analysis.py) for guidance on performing fine-grained model selection after running `run_forecast`.
+# MAGIC Refer to the [notebook](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/post-evaluation-analysis.ipynb) for guidance on performing fine-grained model selection after running `run_forecast`.
 
 # COMMAND ----------
 

diff --git a/examples/monthly/global_monthly.py b/examples/monthly/global_monthly.py
@@ -184,7 +184,7 @@ def transform_group(df):
 # COMMAND ----------
 
 # MAGIC %md
-# MAGIC Refer to the [notebook](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/post-evaluation-analysis.py) for guidance on performing fine-grained model selection after running `run_forecast`.
+# MAGIC Refer to the [notebook](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/post-evaluation-analysis.ipynb) for guidance on performing fine-grained model selection after running `run_forecast`.
 
 # COMMAND ----------
 

diff --git a/examples/monthly/local_univariate_monthly.py b/examples/monthly/local_univariate_monthly.py
@@ -224,7 +224,7 @@ def transform_group(df):
 # COMMAND ----------
 
 # MAGIC %md
-# MAGIC Refer to the [notebook](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/post-evaluation-analysis.py) for guidance on performing fine-grained model selection after running `run_forecast`.
+# MAGIC Refer to the [notebook](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/post-evaluation-analysis.ipynb) for guidance on performing fine-grained model selection after running `run_forecast`.
 
 # COMMAND ----------
 

diff --git a/examples/post-evaluation-analysis.py → examples/post-evaluation-analysis.ipynb b/examples/post-evaluation-analysis.py → examples/post-evaluation-analysis.ipynb
@@ -4,10 +4,7 @@
    "cell_type": "markdown",
    "metadata": {
     "application/vnd.databricks.v1+cell": {
-     "cellMetadata": {
-      "byteLimit": 2048000,
-      "rowLimit": 10000
-     },
+     "cellMetadata": {},
      "inputWidgets": {},
      "nuid": "1e2ce813-1aee-44e9-9517-f98e63f23a4b",
      "showTitle": false,
@@ -47,10 +44,7 @@
    "cell_type": "markdown",
    "metadata": {
     "application/vnd.databricks.v1+cell": {
-     "cellMetadata": {
-      "byteLimit": 2048000,
-      "rowLimit": 10000
-     },
+     "cellMetadata": {},
      "inputWidgets": {},
      "nuid": "8f004c3a-03ee-4a1a-9587-7e69e9cf505c",
      "showTitle": false,
@@ -93,10 +87,7 @@
    "cell_type": "markdown",
    "metadata": {
     "application/vnd.databricks.v1+cell": {
-     "cellMetadata": {
-      "byteLimit": 2048000,
-      "rowLimit": 10000
-     },
+     "cellMetadata": {},
      "inputWidgets": {},
      "nuid": "768b3d5c-f351-43b1-b49a-6356df69a2dd",
      "showTitle": false,
@@ -140,10 +131,7 @@
    "cell_type": "markdown",
    "metadata": {
     "application/vnd.databricks.v1+cell": {
-     "cellMetadata": {
-      "byteLimit": 2048000,
-      "rowLimit": 10000
-     },
+     "cellMetadata": {},
      "inputWidgets": {},
      "nuid": "824a3595-1493-4e60-981d-5945f2dc08cf",
      "showTitle": false,
@@ -252,10 +240,7 @@
    "cell_type": "markdown",
    "metadata": {
     "application/vnd.databricks.v1+cell": {
-     "cellMetadata": {
-      "byteLimit": 2048000,
-      "rowLimit": 10000
-     },
+     "cellMetadata": {},
      "inputWidgets": {},
      "nuid": "183eb27a-a19a-4967-aa65-26b6f5e8ce11",
      "showTitle": false,

diff --git a/examples/weekly/foundation_weekly.py b/examples/weekly/foundation_weekly.py
@@ -181,7 +181,7 @@ def transform_group(df):
 # COMMAND ----------
 
 # MAGIC %md
-# MAGIC Refer to the [notebook](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/post-evaluation-analysis.py) for guidance on performing fine-grained model selection after running `run_forecast`.
+# MAGIC Refer to the [notebook](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/post-evaluation-analysis.ipynb) for guidance on performing fine-grained model selection after running `run_forecast`.
 
 # COMMAND ----------
 

diff --git a/examples/weekly/global_weekly.py b/examples/weekly/global_weekly.py
@@ -175,7 +175,7 @@ def transform_group(df):
 # COMMAND ----------
 
 # MAGIC %md
-# MAGIC Refer to the [notebook](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/post-evaluation-analysis.py) for guidance on performing fine-grained model selection after running `run_forecast`.
+# MAGIC Refer to the [notebook](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/post-evaluation-analysis.ipynb) for guidance on performing fine-grained model selection after running `run_forecast`.
 
 # COMMAND ----------
 

diff --git a/examples/weekly/local_univariate_weekly.py b/examples/weekly/local_univariate_weekly.py
@@ -213,7 +213,7 @@ def transform_group(df):
 # COMMAND ----------
 
 # MAGIC %md
-# MAGIC Refer to the [notebook](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/post-evaluation-analysis.py) for guidance on performing fine-grained model selection after running `run_forecast`.
+# MAGIC Refer to the [notebook](https://github.com/databricks-industry-solutions/many-model-forecasting/blob/main/examples/post-evaluation-analysis.ipynb) for guidance on performing fine-grained model selection after running `run_forecast`.
 
 # COMMAND ----------