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| 1 | +{ |
| 2 | + "cells": [ |
| 3 | + { |
| 4 | + "cell_type": "markdown", |
| 5 | + "metadata": {}, |
| 6 | + "source": [ |
| 7 | + "# Automating Machine Learning with Python and Azure\n", |
| 8 | + "By [Matt Eland](https://MattEland.dev) | [@IntegerMan](https://twitter.com/IntegerMan)" |
| 9 | + ] |
| 10 | + }, |
| 11 | + { |
| 12 | + "cell_type": "markdown", |
| 13 | + "metadata": {}, |
| 14 | + "source": [ |
| 15 | + "## Who am I?\n", |
| 16 | + "\n", |
| 17 | + "After 20+ years of software engineering and engineering management I now teach professionally and do many things including: \n", |
| 18 | + "\n", |
| 19 | + "- Teaching Software Development @ [Tech Elevator](https://TechElevator.com) in Columbus, OH\n", |
| 20 | + "- Speaking at [Conference & User Groups](https://sessionize.com/matt-eland)\n", |
| 21 | + "- Pursuing a Master's Degree in [Data Analytics through Franklin University](https://www.franklin.edu/degrees/masters/data-analytics) _(Anticipated Graduation: Summer of 2024)_\n", |
| 22 | + "- Data Science experiments for fun!\n", |
| 23 | + "- Data Science blogging at [AccessibleAI.dev](https://accessibleai.dev)\n", |
| 24 | + "- Data Science YouTube content at [MattOnDataScience.com](https://MattOnDataScience.com)\n", |
| 25 | + "- Co-organizing the [Central Ohio .NET Developer Group](https://condg.org)\n", |
| 26 | + "\n", |
| 27 | + "I also have several additional qualifications that relate to this talk:\n", |
| 28 | + "\n", |
| 29 | + "- [Microsoft Certified: Azure Data Scientist Associate](https://www.credly.com/badges/53ace869-2160-4fcd-be00-271bc5ada4aa/public_url)\n", |
| 30 | + "- [Microsoft Certified: Azure AI Fundamentals](https://www.credly.com/badges/bf234824-94ff-4d28-8080-cf010ff5408e/public_url)\n", |
| 31 | + "- [IBM Data Scientist Professional Certificate](https://www.credly.com/earner/earned/badge/e2625f64-3fe5-4028-9db2-2a28dabf6405)\n", |
| 32 | + "- [Machine Learning Engineer with Microsoft Azure Nanodegree](https://confirm.udacity.com/DWWTFM2U)\n" |
| 33 | + ] |
| 34 | + }, |
| 35 | + { |
| 36 | + "cell_type": "markdown", |
| 37 | + "metadata": {}, |
| 38 | + "source": [ |
| 39 | + "## What's this talk about?\n", |
| 40 | + "\n", |
| 41 | + "**Machine Learning in Azure using Python**. Seems kinda obvious.\n", |
| 42 | + "\n", |
| 43 | + "Some specific areas we'll cover:\n", |
| 44 | + "\n", |
| 45 | + "- What is Azure Machine Learning Studio?\n", |
| 46 | + "- What is the Azure ML SDK?\n", |
| 47 | + "- Why would I even want to use Azure for this?\n", |
| 48 | + "- What does Python code look like for working with Azure?\n", |
| 49 | + "\n", |
| 50 | + "For the bulk of this talk, we'll explore a **regression experiment** on hockey penalty prediction from data exploration to automated machine learning to model deployment and consumption.\n", |
| 51 | + "\n", |
| 52 | + "We'll also look briefly at a prior **classification experiment** on Die Hard to see the types of metrics available when looking at classification experiments." |
| 53 | + ] |
| 54 | + }, |
| 55 | + { |
| 56 | + "cell_type": "markdown", |
| 57 | + "metadata": {}, |
| 58 | + "source": [ |
| 59 | + "## Touring Azure Machine Learning Studio\n", |
| 60 | + "\n", |
| 61 | + "Let's take a look at Azure Machine Learning Studio to see what the Azure ML SDK allows us to automate.\n", |
| 62 | + "\n", |
| 63 | + "" |
| 64 | + ] |
| 65 | + }, |
| 66 | + { |
| 67 | + "cell_type": "markdown", |
| 68 | + "metadata": {}, |
| 69 | + "source": [ |
| 70 | + "## What is the Azure ML SDK?\n", |
| 71 | + "\n", |
| 72 | + "A way of interacting with the Azure ML SDK from Python code.\n", |
| 73 | + "\n", |
| 74 | + "It allows you to largely bypass the Azure Portal and can integrate more easily into other workflows.\n", |
| 75 | + "\n", |
| 76 | + "To install the Azure ML SDK, you should follow [Microsoft's official documentation](https://docs.microsoft.com/en-us/python/api/overview/azure/ml/installv2?view=azure-ml-py) as the ML SDK continues to evolve." |
| 77 | + ] |
| 78 | + }, |
| 79 | + { |
| 80 | + "cell_type": "markdown", |
| 81 | + "metadata": {}, |
| 82 | + "source": [ |
| 83 | + "## When would I use Azure ML?\n", |
| 84 | + "\n", |
| 85 | + "- When you don't know which algorithms to evaluate\n", |
| 86 | + "- When you want to avoid or defer learning curves around data science libraries\n", |
| 87 | + "- When you want easy access to advanced metrics, explanations, and visualizations\n", |
| 88 | + "- When you want to version your datasets and models in the cloud\n", |
| 89 | + "- When you want to be able to easily share the results of experiments \n", |
| 90 | + "- When you need extra computing power for ML paid at a usage level\n", |
| 91 | + "- When you want to deploy, monitor, and scale your trained models" |
| 92 | + ] |
| 93 | + } |
| 94 | + ], |
| 95 | + "metadata": { |
| 96 | + "interpreter": { |
| 97 | + "hash": "a4868653bb6f8972e87e4c446ab8a445a15b25dedb8594cc74c480f8152ea86a" |
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| 99 | + "kernelspec": { |
| 100 | + "display_name": "Python 3.8.8 64-bit", |
| 101 | + "language": "python", |
| 102 | + "name": "python3" |
| 103 | + }, |
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| 106 | + "name": "ipython", |
| 107 | + "version": 3 |
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| 109 | + "file_extension": ".py", |
| 110 | + "mimetype": "text/x-python", |
| 111 | + "name": "python", |
| 112 | + "nbconvert_exporter": "python", |
| 113 | + "pygments_lexer": "ipython3", |
| 114 | + "version": "3.8.8" |
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