Merge branch 'main' of https://github.com/microsoft/Data-Science-For-Beginners into main

5-Data-Science-In-Cloud/19-tbd/notebook.ipynb

@@ -0,0 +1,88 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Data Science in the Cloud: The \"Azure ML SDK\" way \n",
+    "\n",
+    "## Introduction\n",
+    "\n",
+    "In this notebook, we will learn how to use the Azure ML SDK to train, deploy and consume a model through Azure ML.\n",
+    "\n",
+    "Pre-requisites:\n",
+    "1. You created an Azure ML workspace.\n",
+    "2. You loaded the [Heart Failure dataset](https://www.kaggle.com/andrewmvd/heart-failure-clinical-data) into Azure ML.\n",
+    "3. You uploaded this notebook into Azure ML Studio.\n",
+    "\n",
+    "The next steps are:\n",
+    "\n",
+    "1. Create an Experiment in an existing Workspace.\n",
+    "2. Create a Compute cluster.\n",
+    "3. Load the dataset.\n",
+    "4. Configure AutoML using AutoMLConfig.\n",
+    "5. Run the AutoML experiment.\n",
+    "6. Explore the results and get the best model.\n",
+    "7. Register the best model.\n",
+    "8. Deploy the best model.\n",
+    "9. Consume the endpoint.\n",
+    "\n",
+    "## Azure Machine Learning SDK-specific imports"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "from azureml.core import Workspace"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Initialize Workspace\n",
+    "Initialize a workspace object from persisted configuration. Make sure the config file is present at .\\config.json"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "ws = Workspace.from_config()\n",
+    "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Create an Azure ML experiment\n",
+    "\n",
+    "Let's create an experiment named 'aml-experiment' in the workspace we just initialized."
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "experiment_name = 'aml-experiment'\n",
+    "experiment = Experiment(ws, experiment_name)"
+   ],
+   "outputs": [],
+   "metadata": {}
+  }
+ ],
+ "metadata": {
+  "orig_nbformat": 4,
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

5-Data-Science-In-Cloud/README.md

@@ -2,6 +2,8 @@
 
 ![cloud-picture](img/cloud-picture.jpg)
 
+Photo by [Jelleke Vanooteghem](https://unsplash.com/@ilumire) from [Unsplash](https://unsplash.com/s/photos/cloud?orientation=landscape)
+
 When it comes to doing data science with big data, the cloud can be a game changer. In the next three lessons, we are going to see what the cloud is and why it can be very helpful. We are also going to explore a heart failure dataset and build a model to help assess the probability of someone having a heart failure. We will use the power of the cloud to train, deploy and consume a model in two different ways. One way using only the user interface in a Low code/No code fashion, the other way using the Azure Machine Learning Software Developer Kit (Azure ML SDK).
 
 ![project-schema](19-tbd/img/project-schema.PNG)