diff --git a/.github/workflows/cml.yaml b/.github/workflows/cml.yaml
new file mode 100644
index 0000000..baa7c87
--- /dev/null
+++ b/.github/workflows/cml.yaml
@@ -0,0 +1,26 @@
+name: mlops-exaple-tensorflow-regression
+on: [push]
+jobs:
+  run:
+    runs-on: [ubuntu-latest]
+    container: docker://dvcorg/cml-py3:latest
+    steps:
+      - uses: actions/checkout@v2
+      - name: 'Train my model'
+        env:
+          repo_token: ${{ secrets.GITHUB_TOKEN }}
+        run: |
+
+          # Your ML workflow goes here
+          pip install -r requirements.txt
+          python model.py
+          
+          echo "## Model Metrics" > report.md
+          cat metrics.txt >> report.md
+          
+          echo "\n## Model Performance" >> report.md
+          echo "Model performance metrics are on the plot below." >> report.md
+          
+          cml-publish model_results.png --md >> report.md
+          
+          cml-send-comment report.md
diff --git a/model.py b/model.py
index 31d64c4..09fb93c 100644
--- a/model.py
+++ b/model.py
@@ -9,7 +9,7 @@ def plot_predictions(train_data, train_labels,  test_data, test_labels,  predict
   """
   Plots training data, test data and compares predictions.
   """
-  plt.figure(figsize=(10, 7))
+  plt.figure(figsize=(6, 5))
   # Plot training data in blue
   plt.scatter(train_data, train_labels, c="b", label="Training data")
   # Plot test data in green
@@ -75,7 +75,7 @@ def mse(y_test, y_pred):
 # Create a model using the Sequential API
 model = tf.keras.Sequential([
     tf.keras.layers.Dense(1), 
-    #tf.keras.layers.Dense(1)
+    tf.keras.layers.Dense(1)
     ])
 
 # Compile the model
@@ -84,12 +84,12 @@ def mse(y_test, y_pred):
               metrics = ['mae'])
 
 # Fit the model
-model.fit(X_train, y_train, epochs=50)
+model.fit(X_train, y_train, epochs=100)
 
 
 # Make and plot predictions for model_1
 y_preds = model.predict(X_test)
-#plot_predictions(train_data=X_train, train_labels=y_train,  test_data=X_test, test_labels=y_test,  predictions=y_preds)
+plot_predictions(train_data=X_train, train_labels=y_train,  test_data=X_test, test_labels=y_test,  predictions=y_preds)
 
 
 # Calculate model_1 metrics
@@ -98,5 +98,5 @@ def mse(y_test, y_pred):
 print(f'\nMean Absolute Error = {mae_1}, Mean Squared Error = {mse_1}.')
 
 # Write metrics to file
-#with open('metrics.txt', 'w') as outfile:
-#    outfile.write(f'\nMean Absolute Error = {mae_1}, Mean Squared Error = {mse_1}.')
+with open('metrics.txt', 'w') as outfile:
+    outfile.write(f'\nMean Absolute Error = {mae_1}, Mean Squared Error = {mse_1}.')