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by Veljko Krunic
Succeeding with AI
Copyright
Brief Table of Contents
Table of Contents
Preface
Acknowledgments
About This Book
Who should read this book
How this book is organized
liveBook discussion forum
About the Author
About the Cover Illustration
Chapter 1. Introduction
1.1. Whom is this book for?
1.2. AI and the Age of Implementation
1.3. How do you make money with AI?
1.4. What matters for your project to succeed?
1.5. Machine learning from 10,000 feet
1.6. Start by understanding the possible business actions
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
Summary
Chapter 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
2.5. Finding domain actions
2.6. Overview of AI capabilities
2.7. Introducing unicorns
2.8. Exercises
Summary
Chapter 3. Choosing your first AI project
3.1. Choosing the right projects for a young AI team
3.2. Prioritizing AI projects
3.3. Your first project and first research question
3.4. Pitfalls to avoid
3.5. Exercises
Summary
Chapter 4. Linking business and technology
4.1. A project can’t be stopped midair
4.2. Linking business problems and research questions
4.3. Measuring progress on AI projects
4.4. Linking technical progress with a business metric
4.5. Organizational considerations
4.6. Exercises
Summary
Chapter 5. What is an ML pipeline, and how does it affect an AI project?
5.1. How is an AI project different?
5.2. Why we need to analyze the ML pipeline
5.3. What’s the role of AI methods?
5.4. Balancing data, AI methods, and infrastructure
5.5. Exercises
Summary
Chapter 6. Analyzing an ML pipeline
6.1. Why you should care about analyzing your ML pipeline
6.2. Economizing resources: The E part of CLUE
6.3. MinMax analysis: Do you have the right ML pipeline?
6.4. How to interpret MinMax analysis results
6.5. How to perform an analysis of the ML pipeline
6.6. FAQs about MinMax analysis
6.7. Exercises
Summary
Chapter 7. Guiding an AI project to success
7.1. Improving your ML pipeline with sensitivity analysis
7.2. We’ve completed CLUE
7.3. Advanced methods for sensitivity analysis
7.4. How your AI project evolves through time
7.5. Concluding your AI project
7.6. Exercises
Summary
Chapter 8. AI trends that may affect you
8.1. What is AI?
8.2. AI in physical systems
8.3. AI doesn’t learn 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’ve learned isn’t limited to AI
8.8. Guiding AI to business results
8.9. Exercises
Summary
Appendix A. Glossary of terms
Appendix B. Exercise solutions
B.1. Answers to chapter 1 exercises
B.2. Answers to chapter 2 exercises
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
Data + AI + CLUE = Profit
Index
List of Figures
List of Tables
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Succeeding with AI: How to make AI work for your business
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