Managing Successful AI Projects
Veljko Krunic
  • MEAP began October 2019
  • Publication in Spring 2020 (estimated)
  • ISBN 9781617296932
  • 259 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
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.
Table of Contents detailed table of contents

PART 1 Title

Introduction

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

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

6 Analyzing a machine learning pipeline

7 Guiding an AI project to success

8 AI trends that may affect you

Appendixes

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 Answers to the true/false questions from section 2.8.1

B.2.2 Answers to the scenario-based questions from section 2.8.2

B.3 Answers to chapter 3 exercises

Appendix C: Bibliography

About the Technology

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. Managing Successful AI Projects 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.

About the book

In Managing Successful AI Projects, author and AI consultant Veljko Krunic reveals secrets for succeeding in AI that he developed with Fortune 500 companies, early-stage startups, and other business 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 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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