Succeeding with AI

288 Pages·2020·4.224 MB·English

by  Veljko Krunic

How to make AI work for your business
Veljko Krunic
M A N N I N G
Succeeding with AI
ii
Succeeding with AI
HOW TO MAKE AI WORK FOR YOUR BUSINESS
VELJKO KRUNIC
MANNING
SHELTER ISLAND
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brief contents
1 ■ Introduction 1
2 ■ How to use AI in your business 26
3 ■ Choosing your first AI project 53
4 ■ Linking business and technology 82
5 ■ What is an ML pipeline, and how does it affect an AI project? 112
6 ■ Analyzing an ML pipeline 135
7 ■ Guiding an AI project to success 165
8 ■ AI trends that may affect you 195
v
vi BRIEF CONTENTS
contents
preface xiii
acknowledgments xv
about this book xvii
about the author xxi
about the cover illustration xxii
1 Introduction 1
1.1 Whom is this book for? 2
1.2 AI and the Age of Implementation 4
1.3 How do you make money with AI? 6
1.4 What matters for your project to succeed? 7
1.5 Machine learning from 10,000 feet 8
1.6 Start by understanding the possible business actions 11
1.7 Don’t fish for “something in the data” 13
1.8 AI finds correlations, not causes! 15
1.9 Business results must be measurable! 16
1.10 What is CLUE? 19
1.11 Overview of how to select and run AI projects 21
1.12 Exercises 23
True/False questions 24 ■ Longer exercises: Identify the
problem 24
vii
viii CONTENTS
2 How to use AI in your business 26
2.1 What do you need to know about AI? 27
2.2 How is AI used? 29
2.3 What’s new with AI? 31
2.4 Making money with AI 33
AI applied to medical diagnosis 34 ■ General principles for
monetizing AI 36
2.5 Finding domain actions 38
AI as part of the decision support system 39 ■ AI as a
part of a larger product 40 ■ Using AI to automate part
of the business process 42 ■ AI as the product 43
2.6 Overview of AI capabilities 45
2.7 Introducing unicorns 47
Data science unicorns 47 ■ What about data engineers? 48
So where are the unicorns? 49
2.8 Exercises 50
Short answer questions 51 ■ Scenario-based
questions 51
3 Choosing your first AI project 53
3.1 Choosing the right projects for a young AI team 54
The look of success 54 ■ The look of failure 57
3.2 Prioritizing AI projects 59
React: Finding business questions for AI to answer 60
Sense/Analyze: AI methods and data 63 ■ Measuring
AI project success with business metrics 65 ■ Estimating
AI project difficulty 68
3.3 Your first project and first research question 69
Define the research question 70 ■ If you fail,
fail fast 74
3.4 Pitfalls to avoid 74
Failing to build a relationship with the business team 75
Using transplants 75 ■ Trying moonshots without
the rockets 76 ■ It’s about using advanced tools to look
at the sea of data 77 ■ Using your gut feeling instead
of CLUE 78
3.5 Exercises 80
CONTENTS ix
4 Linking business and technology 82
4.1 A project can’t be stopped midair 83
What constitutes a good recommendation engine? 83 ■ What is gut
feeling? 85
4.2 Linking business problems and research questions 85
Introducing the L part of CLUE 86 ■ Do you have the right
research question? 87 ■ What questions should a metric be able
to answer? 87 ■ Can you make business decisions based on a
technical metric? 88 ■ A metric you don’t understand is a poor
business metric 91 ■ You need the right business metric 93
4.3 Measuring progress on AI projects 94
4.4 Linking technical progress with a business metric 96
Why do we need technical metrics? 97 ■ What is the profit
curve? 97 ■ Constructing a profit curve for bike rentals 99
Why is this not taught in college? 102 ■ Can’t businesses define
the profit curve themselves? 103 ■ Understanding technical
results in business terms 105
4.5 Organizational considerations 106
Profit curve precision depends on the business problem 106
A profit curve improves over time 107 ■ It’s about learning,
not about being right 108 ■ Dealing with information
hoarding 108 ■ But we can’t measure that! 109
4.6 Exercises 110
5 What is an ML pipeline, and how does it affect an AI
project? 112
5.1 How is an AI project different? 113
The ML pipeline in AI projects 113 ■ Challenges the AI system
shares with a traditional software system 117 ■ Challenges
amplified in AI projects 117 ■ Ossification of the ML
pipeline 118 ■ Example of ossification of an ML pipeline 121
How to address ossification of the ML pipeline 123
5.2 Why we need to analyze the ML pipeline 126
Algorithm improvement: MNIST example 126 ■ Further
examples of improving the ML pipeline 127 ■ You must
analyze the ML pipeline! 128
5.3 What’s the role of AI methods? 129
5.4 Balancing data, AI methods, and infrastructure 131
5.5 Exercises 133