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