Update myenv.yml

Creating a readme which outlines the content here for the CLI

cli/myenv.yml

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+name: project_environment
+dependencies:
+  - python=3.6.2
+  - scikit-learn
+  - numpy
+
+  - pip:
+    - numpy==1.14.2
+    - pandas
+    - scipy==1.0.0
+    - scikit-learn==0.19.1
+    # Required packages for AzureML execution, history, and data preparation.
+    - --index-url https://azuremlsdktestpypi.azureedge.net/sdk-release/Preview/E7501C02541B433786111FE8E140CAA1
+    - --extra-index-url https://pypi.python.org/simple
+    - azureml-defaults

cli/readme.md

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+# CLI Example Content
+This content can be used in conjunction with our [CLI reference guide.](https://docs.microsoft.com/en-us/azure/machine-learning/service/reference-azure-machine-learning-cli)
+
+Example content includes training scripts, conda environment files and scoring files.

cli/score.py

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+import pickle
+import json
+import numpy
+from sklearn.externals import joblib
+from sklearn.linear_model import Ridge
+from azureml.core.model import Model
+
+def init():
+    global model
+    # note here "sklearn_regression_model.pkl" is the name of the model registered under
+    # this is a different behavior than before when the code is run locally, even though the code is the same.
+    model_path = Model.get_model_path('sklearn_regression_model.pkl')
+    # deserialize the model file back into a sklearn model
+    model = joblib.load(model_path)
+
+# note you can pass in multiple rows for scoring
+def run(raw_data):
+    try:
+        data = json.loads(raw_data)['data']
+        data = numpy.array(data)
+        result = model.predict(data)
+    except Exception as e:
+        result = str(e)
+    return json.dumps({"result": result.tolist()})

cli/train.py

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+# Copyright (c) Microsoft. All rights reserved.
+# Licensed under the MIT license.
+
+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 sklearn.externals import joblib
+import os
+import numpy as np
+
+os.makedirs('./outputs', exist_ok=True)
+
+X, y = load_diabetes(return_X_y=True)
+
+run = Run.get_submitted_run()
+
+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 = np.arange(0.0, 1.0, 0.05)
+
+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)
+
+    model_file_name = 'ridge_{0:.2f}.pkl'.format(alpha)
+    # save model in the outputs folder so it automatically get uploaded
+    with open(model_file_name, "wb") as file:
+        joblib.dump(value=reg, filename=os.path.join('./outputs/',
+                                                     model_file_name))
+
+    print('alpha is {0:.2f}, and mse is {1:0.2f}'.format(alpha, mse))