Video descriptionChock-full of illuminating examples that will dramatically improve your success with AI projects.
Zarak Mahmud, Techflo
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.
about the technology
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.
about the book
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.
- Where to invest for maximum payoff
- How AI projects are different from other software projects
- Catching early warnings in time to correct course
- Exercises and examples based on real-world business dilemmas
about the audience
For project and business leadership, result-focused data scientists, and engineering teams. No AI knowledge required.
about the author
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.
David Paccoud, Bioclinica
A definitive resource for building an AI system idea...and deploying it in production.
Teresa Fontanella De Santis, Accenture
Follow this book’s advice, and you will find your organization "succeeding with AI"!
James J. Byleckie, BH Enterprises
NARRATED BY NATE COLITTO
Table of contents
- 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
- Title: Succeeding with AI video edition
- Release date: March 2020
- Publisher(s): Manning Publications
- ISBN: None
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