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 …
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.
Succeeding with AI requires talent, tools, and money. So why do many well-funded, state-of-the-art projects fail to deliver meaningful business value? Because talent, tools, and money aren’t enough: You also need to know how to ask the right questions. In this unique book, AI consultant Veljko Krunic reveals a tested process to start AI projects right, so you’ll get the results you want.
Succeeding with AI sets out a framework for planning and running cost-effective, reliable AI projects that produce real business results. This practical guide reveals secrets forged during the author’s experience with dozens of startups, established businesses, and Fortune 500 giants that will help you establish meaningful, achievable goals. In it you’ll master a repeatable process to maximize the return on data-scientist hours and learn to implement effectiveness metrics for keeping projects on track and resistant to calcification.
For project and business leadership, result-focused data scientists, and engineering teams. No AI knowledge required.
Veljko Krunic is a data science consultant, has a computer science PhD, and is a certified Six Sigma Master Black Belt.
If you are starting a new AI project, put all odds on your side by reading this book.Chapter 1. Introduction
Chapter 1. AI and the Age of Implementation
Chapter 1. Machine learning from 10,000 feet
Chapter 1. Start by understanding the possible business actions
Chapter 1. AI finds correlations, not causes!
Chapter 1. What is CLUE?
Chapter 1. Exercises
Chapter 2. How to use AI in your business
Chapter 2. How is AI used?
Chapter 2. Making money with AI
Chapter 2. Finding domain actions
Chapter 2. AI as a part of a larger product
Chapter 2. Overview of AI capabilities
Chapter 2. Introducing unicorns
Chapter 2. Exercises
Chapter 3. Choosing your first AI project
Chapter 3. Prioritizing AI projects
Chapter 3. Measuring AI project success with business metrics
Chapter 3. Your first project and first research question
Chapter 3. Pitfalls to avoid
Chapter 3. Using your gut feeling instead of CLUE
Chapter 4. Linking business and technology
Chapter 4. Linking business problems and research questions
Chapter 4. A metric you don’t understand is a poor business metric
Chapter 4. Measuring progress on AI projects
Chapter 4. Linking technical progress with a business metric
Chapter 4. Why is this not taught in college?
Chapter 4. Organizational considerations
Chapter 5. What is an ML pipeline, and how does it affect an AI project?
Chapter 5. Challenges the AI system shares with a traditional software system
Chapter 5. Example of ossification of an ML pipeline
Chapter 5. How to address ossification of the ML pipeline
Chapter 5. Why we need to analyze the ML pipeline
Chapter 5. What’s the role of AI methods?
Chapter 5. Balancing data, AI methods, and infrastructure
Chapter 6. Analyzing an ML pipeline
Chapter 6. Economizing resources: The E part of CLUE
Chapter 6. How to interpret MinMax analysis results
Chapter 6. What if your ML pipeline needs improvement?
Chapter 6. How to perform an analysis of the ML pipeline
Chapter 6. Performing the Max part of MinMax analysis
Chapter 6. Estimates and safety factors in MinMax analysis
Chapter 6. Dealing with complex profit curves
Chapter 6. FAQs about MinMax analysis
Chapter 7. Guiding an AI project to success
Chapter 7. Performing local sensitivity analysis
Chapter 7. We’ve completed CLUE
Chapter 7. Advanced methods for sensitivity analysis
Chapter 7. How to address the interactions between ML pipeline stages
Chapter 7. One common objection you might encounter
Chapter 7. How to analyze the stage that produces data
Chapter 7. How your AI project evolves through time
Chapter 7. Concluding your AI project
Chapter 8. AI trends that may affect you
Chapter 8. AI in physical systems
Chapter 8. IoT devices and AI systems must play well together
Chapter 8. AI doesn’t learn causality, only correlations
Chapter 8. How are AI errors different from human mistakes?
Chapter 8. AutoML is approaching
Chapter 8. Guiding AI to business results
Appendix B. Exercise solutions
Appendix B. Answers to chapter 2 exercises
Appendix B. Answers to chapter 3 exercises
Appendix B. Answers to chapter 6 exercises
Appendix B. Answers to chapter 7 exercises