Merge branch 'master' of github.com:Azure-Samples/azure-python-labs-pr

Author: asw101

6-azureml-movie-recommendation/README.md

@@ -1,4 +1,6 @@
 # Build a Movie Recommendation System with Azure Machine Learning service
+Get started in Azure Notebooks[![Azure Notebooks](https://notebooks.azure.com/launch.svg)]
+
 Recommendation systems are used in a variety of industries, from retail to news and media. If you’ve ever used a streaming service or ecommerce site that has surfaced recommendations for you based on what you’ve previously watched or purchased, you’ve interacted with a recommendation system. With the availability of large amounts of data, many businesses are turning to recommendation systems as a critical revenue driver. However, finding the right recommender algorithms can be very time consuming for data scientists. This is why Microsoft has provided a [GitHub repository](https://github.com/Microsoft/Recommenders) with Python best practice examples to facilitate the building and evaluation of recommendation systems. You can learn more about the repo on the [Azure Blog](https://azure.microsoft.com/en-us/blog/building-recommender-systems-with-azure-machine-learning-service/).
 
 This tutorial will walk through how to build a Movie Recommender system trained with a Simple Algorithm for Recommenders (SAR) for the [Movielens dataset](https://grouplens.org/datasets/movielens/) on [Azure Machine Learning service](https://docs.microsoft.com/azure/machine-learning/service/overview-what-is-azure-ml). It demonstrates how to use the power of the cloud to manage data, switch to powerful GPU machines, and monitor runs while training a model. You will also be able to test an existing webservice and find the most relevant movie recommendations.

6-azureml-movie-recommendation/sar_movielens_with_azureml.ipynb

@@ -16,7 +16,7 @@
       },
       "cell_type": "code",
       "source": "import sys\nimport os\nimport shutil\nimport numpy as np\n\nfrom reco_utils.dataset import movielens\nfrom reco_utils.azureml.azureml_utils import get_or_create_workspace\n\nimport azureml\nfrom azureml.core import Workspace, Run, Experiment\nfrom azureml.core.compute import ComputeTarget, AmlCompute\nfrom azureml.train.estimator import Estimator\nfrom azureml.widgets import RunDetails",
-      "execution_count": 1,
+      "execution_count": 2,
       "outputs": []
     },
     {
@@ -26,18 +26,13 @@
     },
     {
       "metadata": {
-        "trusted": true
+        "trusted": true,
+        "scrolled": true
       },
       "cell_type": "code",
-      "source": "ws = Workspace.from_config()",
-      "execution_count": 9,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": "Found the config file in: /home/nbuser/library/config.json\n",
-          "name": "stdout"
-        }
-      ]
+      "source": "ws = get_or_create_workspace(subscription_id=\"d5aa990f-2452-4701-bd8e-21959f91194c\",resource_group=\"190500-labs-azureml\",workspace_name= \"pycon_azureml\")",
+      "execution_count": null,
+      "outputs": []
     },
     {
       "metadata": {},