code: # new update
  
  import pickle
  import os
  import numpy as np
  from sklearn.datasets import load_diabetes
  from sklearn.linear_model import Ridge
  from sklearn.metrics import mean_squared_error
  from sklearn.model_selection import train_test_split
  from azureml.core.run import Run
  
  from utils import mylib
  
  os.makedirs('./outputs', exist_ok=True)
  
  X, y = load_diabetes(return_X_y=True)
  
  run = Run.get_context()
  
  X_train, X_test, y_train, y_test = train_test_split(X, y,
                                                      test_size=0.2,
                                                      random_state=0)
  data = {"train": {"X": X_train, "y": y_train},
          "test": {"X": X_test, "y": y_test}}
  
  # list of numbers from 0.0 to 1.0 with a 0.05 interval
  alphas = mylib.get_alphas()
  
  for alpha in alphas:
      # Use Ridge algorithm to create a regression model
      reg = Ridge(alpha=alpha)
      reg.fit(data["train"]["X"], data["train"]["y"])
  
      preds = reg.predict(data["test"]["X"])
      mse = mean_squared_error(preds, data["test"]["y"])
      run.log('alpha', alpha)
      run.log('mse', mse)
  
      # Save model in the outputs folder so it automatically get uploaded when running on AML Compute
      model_file_name = 'ridge_{0:.2f}.pkl'.format(alpha)
      with open(os.path.join('./outputs/', model_file_name), 'wb') as file:
          pickle.dump(reg, file)
  
      print('alpha is {0:.2f}, and mse is {1:0.2f}'.format(alpha, mse))
  
apis: sklearn.metrics.mean_squared_error sklearn.model_selection.train_test_split sklearn.datasets.load_diabetes sklearn.linear_model.Ridge