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Succeeding with AI
How to get the results you want
Veljko Krunic
  • MEAP began October 2019
  • Publication in April 2020 (estimated)
  • ISBN 9781617296932
  • 250 pages (estimated)
  • printed in black & white

If you’re a business leader hearing that AI is the new black and are afraid you’re missing out then be sure to read this book before starting!

Sune Lomholt
Companies small and large are initiating AI projects, investing vast sums of money on software, developers, and data scientists. Too often, these AI projects focus on technology at the expense of actionable or tangible business results, resulting in scattershot results and wasted investment. Succeeding with AI sets out a blueprint for AI projects to ensure they are predictable, successful, and profitable. It’s filled with practical techniques for running data science programs that ensure they’re cost effective and focused on the right business goals.
Table of Contents detailed table of contents

PART 1 Title


1.1 Who is this book for?

1.2 AI and the Age of Implementation

1.3 How is money made?

1.4 What matters for your project to succeed?

1.5 Machine learning from 10,000 feet

1.6 Start by understanding possible business actions you can take

1.7 Don’t fish for “something in the data”

1.8 AI finds correlations, not causes!

1.9 Business results must be measurable!

1.10 What is CLUE?

1.11 Overview of how to select and run AI projects

1.12 Exercises

1.12.1 True/False questions

1.12.2 Longer exercises: Identify the problem

1.13 Summary

2 How to use AI in your business

2.1 What do you need to know about AI?

2.2 How is AI used?

2.3 What’s new with AI?

2.4 Making money with AI

2.4.1 AI applied to medical diagnosis

2.4.2 General principles for monetizing AI

2.5 Finding domain actions

2.5.1 AI as part of the decision support system

2.5.2 AI as a part of a larger product

2.5.3 Using AI to automate part of the business process

2.5.4 AI as the product

2.6 Overview of AI capabilities

2.7 Introducing Unicorns

2.7.1 Data science unicorns

2.7.2 What about data engineers?

2.7.3 So where are the unicorns?

2.8 Exercises

2.8.1 True/False questions

2.8.2 Scenario-based questions

2.9 Summary

3 Choosing your first AI project

3.1 Choosing the right projects for a young AI team

3.1.1 The look of success

3.1.2 The look of failure

3.2 Prioritizing AI projects

3.2.1 React: Finding business questions for AI to answer

3.2.2 Sense/Analyze: AI methods and data

3.2.3 Measuring AI project success with business metrics

3.2.4 Estimating AI project difficulty

3.3 Your first project and first research question

3.3.1 Define the research question

3.3.2 If you fail, fail fast

3.4 Pitfalls to Avoid

3.4.1 Failing to build a relationship with the business team

3.4.2 Using transplants

3.4.3 Trying moonshots without the rockets

3.4.4 It is about using advanced tools to look at the sea of data

3.4.5 Using your gut feeling instead of CLUE

3.5 Exercises

3.6 Summary

4 Linking business and technology

4.1 A project can’t be stopped midair

4.2 Linking business problems and research questions

4.2.1 Introducing the L part of CLUE

4.2.2 Do you have the right research question?

4.2.3 What questions a metric should be able answer?

4.2.4 Can you make business decisions based on a technical metric?

4.2.5 A metric you don’t understand is a poor business metric

4.2.6 You need the right business metric

4.3 Measuring progress on AI projects

4.4 Linking technical progress with a business metric

4.4.1 Why do we need technical metrics?

4.4.2 What is the profit curve?

4.4.3 Constructing a profit curve for bike rentals

4.4.4 Why is this not taught in college?

4.4.5 Can’t businesses define profit curve themselves?

4.4.6 Understanding technical results in business terms

4.5 Organizational considerations

4.5.1 Profit curve precision depends on the business problem

4.5.2 A profit curve improves over time

4.5.3 It’s about learning, not about being right

4.5.4 Dealing with information hoarding

4.5.5 But we can’t measure that!

4.6 Exercises

4.7 Summary

5 What is a machine learning pipeline and how does it affect the AI project?

5.1 How is an AI project different?

5.1.1 The ML pipeline in AI projects

5.1.2 Challenges the AI system shares with a traditional software system

5.1.3 Challenges amplified in AI projects

5.1.4 Ossification of the ML pipeline

5.1.5 Example of ossification of an ML pipeline

5.1.6 How to address ossification of the ML pipeline?

5.2 Why we need to analyze the ML pipeline

5.2.1 Algorithm improvement: MNIST example

5.2.2 Should you always clean the data?

5.2.3 Why not improve both data and ML algorithm?

5.2.4 How do you know what to improve?

5.3 What’s the role of AI methods?

5.4 Balancing data, AI methods, and infrastructure

5.5 Exercises

5.6 Summary

6 Analyzing a machine learning pipeline

6.1 Why you should care about analyzing an ML pipeline

6.2 Economizing resources on AI project: The E part of CLUE

6.3 MinMax analysis: Do you have the right ML pipeline?

6.4 How to interpret MinMax analysis’ results

6.4.1 Scenario: The ML pipeline for a smart parking meter

6.4.2 Example of Interpreting the results of a MinMax analysis

6.4.3 What if your ML pipeline needs further improvement?

6.4.4 Rules for interpretating the results of MinMax analysis

6.5 How to perform an analysis of the ML pipeline

6.5.1 Performing the Min part of MinMax analysis

6.5.2 Performing the Max part of MinMax analysis

6.5.3 Estimates and safety factors in MinMax analysis

6.6 FAQs about MinMax analysis

6.6.1 Should MinMax be the first analysis of the ML pipeline?

6.6.2 Which analysis should you perform first? Min or Max?

6.6.3 Should small company or small team skip the MinMax analysis?

6.6.4 Why do you use the term MinMax analysis?

6.7 Exercises

6.8 Summary

7 Guiding an AI project to success

7.1 Improving your ML pipeline with sensitivity analysis

7.1.1 Performing local sensitivity analysis

7.1.2 Global sensitivity analysis

7.1.3 Example of using sensitivity analysis results

7.2 We have completed CLUE

7.3 Advanced methods for sensitivity analysis

7.3.1 Is local sensitivity analysis appropriate for your ML pipeline?

7.3.2 How to address the interactions between the stages in ML pipeline?

7.3.3 Should I use experimental design?

7.3.4 One common objection you might encounter

7.3.5 How to analyze the stage that produces data

7.3.6 What types of sensitivity analysis apply to my project?

7.4 How your AI project evolves through time

7.4.1 Time affects your business results

7.4.2 Improving the ML pipeline over time

7.4.3 Timing diagrams: How business value changes over time

7.5 Concluding your AI project

7.6 Exercises

7.7 Summary

8 AI trends that may affect you

8.1 What is AI?

8.2 AI in physical systems

8.2.1 First, do no harm

8.2.2 IoT devices and AI systems must play well together

8.3 AI doesn’t know causality, only correlations

8.4 Not all data is created equal

8.5 How are AI errors different from human mistakes?

8.6 AutoML is approaching

8.7 What you learned is not limited to AI!

8.8 Guiding AI to business results

8.9 Exercises

8.10 Summary


Appendix A: Glossary of terms

Appendix B: Exercise solutions

B.1 Answers to chapter 1 exercises

B.1.1 True/False questions

B.1.2 Longer exercises: identify the problem

B.2 Answers to chapter 2 exercises

B.2.1 True/False questions

B.2.2 Answers to the scenario-based questions

B.3 Answers to chapter 3 exercises

B.4 Answers to chapter 4 exercises

B.5 Answers to chapter 5 exercises

B.6 Answers to chapter 6 exercises

B.7 Answers to chapter 7 exercises

B.8 Answers to chapter 8 exercises

Appendix C: Bibliography

About the Technology

The big challenge for a successful AI project isn’t deciding which problems you can solve. It’s deciding which problems you should solve. AI systems with great funding and top talent will still fail if they aren’t answering questions that will drive real business value. As the leader of an AI team, it’s your job to make sure you’re directing your team toward the right goals and implementing a process that will deliver results on time and on budget.

About the book

In Succeeding with AI, author and AI consultant Veljko Krunic reveals secrets for succeeding in AI that he developed with Fortune 500 companies, early-stage startups, and other businesses across multiple industries. Veljko first lays out a framework for determining the right questions to answer for your business. Then, he teaches you a repeatable process for properly organizing an AI project to maximize the value of limited sources, such as the time of your data scientists. You’ll learn to establish metrics that let you judge the effectiveness of your machine learning against business needs and how to assess whether your AI project is on the right track early on in its lifecycle. With exercises based on the kind of business dilemmas you’ll encounter in the real world, you’ll learn how to manage an ML pipeline and keep it from change-resistant calcification. When you’re done, you’ll be ready to start investing wisely in data science to deliver concrete, reliable, and profitable results for your business.

What's inside

  • Selecting the right AI project to meet specific business goals
  • Economizing resources to deliver the best value for money
  • How to measure the success of your AI efforts in the business terms
  • Predict if you are you on the right track to deliver your intended business results

About the reader

For executives, managers, team leaders, and business-focused data scientists. No specific technical knowledge or programming skills required.

About the author

Veljko Krunic is an independent data science consultant who has worked with companies that range from startups to Fortune 10 enterprises. He holds a PhD in Computer Science and an MS in Engineering Management, both from the University of Colorado at Boulder. He is also a Six Sigma Master Black Belt.

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