Automated Machine Learning with Microsoft Azure: Build highly accurate and scalable end-to-end AI solutions with Azure AutoML

340 Pages·2021·7.292 MB·English

by  Dennis Michael Sawyers

Automated Machine
Learning with
Microsoft Azure
Build highly accurate and scalable end-to-end AI
solutions with Azure AutoML
Dennis Michael Sawyers
BIRMINGHAM—MUMBAI
Automated Machine Learning with
Microsoft Azure
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To my wife, Kyoko Sawyers, who has always been by my side and supported
me through many long evenings, and to my daughter, Sophia Rose, who was
born halfway through the writing of this book.
– Dennis Sawyers
Contributors
About the author
Dennis Michael Sawyers is a senior cloud solutions architect (CSA) at Microsoft,
specializing in data and AI. In his role as a CSA, he helps Fortune 500 companies leverage
Microsoft Azure cloud technology to build top-class machine learning and AI solutions.
Prior to his role at Microsoft, he was a data scientist at Ford Motor Company in Global
Data Insight and Analytics (GDIA) and a researcher in anomaly detection at the highly
regarded Carnegie Mellon Auton Lab. He received a master's degree in data analytics from
Carnegie Mellon's Heinz College and a bachelor's degree from the University of Michigan.
More than anything, Dennis is passionate about democratizing AI solutions through
automated machine learning technology.
I want to thank the people who have been close to me and supported me,
especially my wife, Kyoko, for encouraging me to finish this book, Rick
Durham and Sam Istephan, for teaching me Azure Machine Learning, and
Sabina Cartacio, Aniththa Umamahesan, and Deepti Mokkapati from the
Microsoft Azure product team for helping me learn the ins and outs
of AutoML.
About the reviewer
Marek Chmel is a senior CSA at Microsoft, specializing in data and AI. He is a speaker
and trainer with more than 15 years' experience. He has been a Data Platform MVP since
2012. He has earned numerous certifications, including Azure Architect, Data Engineer
and Scientist Associate, Certified Ethical Hacker, and several eLearnSecurity certifications.
Marek earned his master's degree in business and informatics from Nottingham Trent
University. He started his career as a trainer for Microsoft Server courses and later worked
as SharePoint team lead and principal database administrator. He has authored two books,
Hands-On Data Science with SQL Server 2017 and SQL Server 2017 Administrator's Guide.

Table of Contents
Preface
Section 1:
AutoML Explained – Why, What, and How
1
Introducing AutoML
Explaining data science's ROI Putting it all together 9
problem 4
Analyzing why AI projects fail
Defining machine learning, data
slowly 10
science, and AI 4
Solving the ROI problem with
Machine learning versus traditional
AutoML 13
software 5
The five steps to machine learning success 6 Summary 15
2
Getting Started with Azure Machine Learning Service
Technical requirements 18 Building compute to run your
Creating your first AMLS AutoML jobs 26
workspace 18 Creating a compute instance 27
Creating an Azure account 18 Creating a compute cluster 30
Creating an AMLS workspace 20 Creating a compute cluster and
compute instance with the Azure CLI 33
Creating an AMLS workspace with code 23
Navigating AML studio 24 Working with data in AMLS 34
ii Table of Contents
Creating a dataset using the GUI 34 engineering 40
Creating a dataset using code 37 Normalizing data for ML with iterative
data transformation 41
Understanding how AutoML
Training models quickly with iterative
works on Azure 38
ML model building 41
Ensuring data quality with data Getting the best results with ML model
guardrails 39 ensembling 42
Improving data with intelligent feature
Summary 42
3
Training Your First AutoML Model
Technical requirements 44 Understanding model metrics 58
Loading data into AMLS for
Explaining your AutoML model 61
AutoML 44
Obtaining better AutoML
Creating an AutoML solution 51
performance 63
Interpreting your AutoML results 56
Summary 64
Understanding data guardrails 57
Section 2:
AutoML for Regression, Classification, and
Forecasting – A Step-by-Step Guide
4
Building an AutoML Regression Solution
Technical requirements 68 Registering your trained
Preparing data for AutoML regression model 83
regression 68 Fine-tuning your AutoML
regression model 85
Setting up your Jupyter environment 69
Preparing your data for AutoML 72 Improving AutoML regression models 85
Understanding AutoML regression
Training an AutoML regression
algorithms 88
model 76
Summary 90
Table of Contents iii
5
Building an AutoML Classification Solution
Technical requirements 94 Training an AutoML multiclass
Prepping data for AutoML model 108
classification 95 Fine-tuning your AutoML
classification model 114
Navigating to your Jupyter environment 95
Loading and transforming your data 97 Improving AutoML classification models 115
Understanding AutoML classification
Training an AutoML
algorithms 117
classification model 101
Summary 119
Registering your trained
classification model 106
6
Building an AutoML Forecasting Solution
Technical requirements 122 Prophet and ARIMA 135
Prepping data for AutoML
Registering your trained
forecasting 123
forecasting model 137
Navigating to your Jupyter environment 123
Fine-tuning your AutoML
Loading and transforming your data 124
forecasting model 139
Training an AutoML forecasting Improving AutoML forecasting models 139
model 128 Understanding AutoML forecasting
algorithms 142
Training a forecasting model with
standard algorithms 129
Summary 143
Training a forecasting model with
7
Using the Many Models Solution Accelerator
Technical requirements 146 Prepping data for many models 150
Installing the many models Prepping the sample OJ dataset 150
solution accelerator 147 Prepping a pandas dataframe 151
Creating a new notebook in your
Training many models
Jupyter environment 148
simultaneously 155
Installing the MMSA from GitHub 148