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!
1.11. Overview of how to select and run AI projects
Chapter 2. How to use AI in your business
2.1. What do you need to know about AI?
2.5.1. AI as part of the decision support system
2.5.2. AI as a part of a larger product
2.6. Overview of AI capabilities
Chapter 3. Choosing your first AI project
3.1. Choosing the right projects for a young AI team
3.2.1. React: Finding business questions for AI to answer
3.2.2. Sense/Analyze: AI methods and data
3.3. Your first project and first research question
3.4.1. Failing to build a relationship with the business team
3.4.3. Trying moonshots without the rockets
3.4.4. It’s about using advanced tools to look at the sea of data
Chapter 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 should a metric be able to 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
4.3. 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?
4.5. 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
Chapter 5. What is an ML pipeline, and how does it affect an 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
5.2. Why we need to analyze the ML pipeline
5.2.1. Algorithm improvement: MNIST example
5.3. What’s the role of AI methods?
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
6.4.1. Scenario: The ML pipeline for a smart parking meter
6.4.2. What if your ML pipeline needs improvement?
6.4.3. Rules for interpreting 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 a small company or small team skip the MinMax analysis?
Chapter 7. Guiding an AI project to success
7.1. Improving your ML pipeline with sensitivity analysis
7.1.1. Performing local sensitivity analysis
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 ML pipeline stages
7.3.3. Should I use design of experiments?
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
Chapter 8. AI trends that may affect you
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?
8.7. What you’ve learned isn’t limited to AI
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
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