turn next actions into bullet points

Author: jotaylo

articles/machine-learning/tutorial-convert-ml-experiment-to-production.md

@@ -37,21 +37,34 @@ from sklearn.linear_model import Ridge
 from sklearn.metrics import mean_squared_error
 from sklearn.model_selection import train_test_split
 import joblib
+import pandas as pd
+
+sample_data = load_diabetes()
 
-X, y = load_diabetes(return_X_y=True)
+df = pd.DataFrame(
+    data=sample_data.data,
+    columns=sample_data.feature_names)
+df['Y'] = sample_data.target
+
+X = df.drop('Y', axis=1).values
+y = df['Y'].values
 
 X_train, X_test, y_train, y_test = train_test_split(
-        X, y, test_size=0.2, random_state=0)
+    X, y, test_size=0.2, random_state=0)
 data = {"train": {"X": X_train, "y": y_train},
         "test": {"X": X_test, "y": y_test}}
 
-alpha = 0.5
+args = {
+    "alpha": 0.5
+}
 
-reg = Ridge(alpha=alpha)
+reg_model = Ridge(**args)
 reg.fit(data["train"]["X"], data["train"]["y"])
 
-preds = reg.predict(data["test"]["X"])
-print("mse", mean_squared_error(preds, data["test"]["y"]))
+preds = reg_model.predict(data["test"]["X"])
+mse = mean_squared_error(preds, y_test)
+metrics = {"mse": mse}
+print(metrics)
 
 model_name = "sklearn_regression_model.pkl"
 joblib.dump(value=reg, filename=model_name)
@@ -505,7 +518,6 @@ def test_train_model():
 
 Now that you understand how to convert from an experiment to production code, see the following links for more information and next steps:
 
-> [!div class="nextstepaction"]
-> [MLOpsPython](https://github.com/microsoft/MLOpsPython/blob/master/docs/custom_model.md): Build a CI/CD pipeline to train, evaluate and deploy your model using Azure Pipelines and Azure Machine Learning
-> [Monitor Azure ML experiment runs and metrics](https://docs.microsoft.com/azure/machine-learning/how-to-track-experiments)
-> [Monitor and collect data from ML web service endpoints](https://docs.microsoft.com/azure/machine-learning/how-to-enable-app-insights)
++ [MLOpsPython](https://github.com/microsoft/MLOpsPython/blob/master/docs/custom_model.md): Build a CI/CD pipeline to train, evaluate and deploy your own model using Azure Pipelines and Azure Machine Learning
++ [Monitor Azure ML experiment runs and metrics](https://docs.microsoft.com/azure/machine-learning/how-to-track-experiments)
++ [Monitor and collect data from ML web service endpoints](https://docs.microsoft.com/azure/machine-learning/how-to-enable-app-insights)