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AI Product Manager's Handbook

You're reading from   AI Product Manager's Handbook Build, integrate, scale, and optimize products to grow as an AI product manager

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Product type Paperback
Published in Nov 2024
Publisher Packt
ISBN-13 9781835882849
Length 484 pages
Edition 2nd Edition
Languages
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Author (1):
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Irene Bratsis Irene Bratsis
Author Profile Icon Irene Bratsis
Irene Bratsis
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Table of Contents (26) Chapters Close

Preface 1. Part 1: Lay of the Land – Terms, Infrastructure, Types of AI, and Products Done Well
2. Understanding the Infrastructure and Tools for Building AI Products FREE CHAPTER 3. Model Development and Maintenance for AI Products 4. Deep Learning Deep Dive 5. Commercializing AI Products 6. AI Transformation and Its Impact on Product Management 7. Part 2: Building an AI-Native Product
8. Understanding the AI-Native Product 9. Productizing the ML Service 10. Customization for Verticals, Customers, and Peer Groups 11. Product Design for the AI-Native Product 12. Benchmarking Performance, Growth Hacking, and Cost 13. Managing the AI-Native Product 14. Part 3: Integrating AI into Existing Traditional Software Products
15. The Rising Tide of AI 16. Trends and Insights Across Industry 17. Evolving Products into AI Products 18. The Role of AI Product Design 19. Managing the Evolving AI Product 20. Part 4: Managing the AI PM Career
21. Starting a Career as an AI PM 22. What Does It Mean to Be a Good AI PM? 23. Maturing and Growing as an AI PM 24. Other Books You May Enjoy
25. Index

Refreshing – the ethics of how often we update our models

When we think about the amazing power we have as humans, the complex brain operations we employ for things such as weighing up different choices or deciding whether or not we can trust someone, we may find it hard or impossible to believe that we could ever use machines to do even a fraction of what our minds can do. Most of us make choices, selections, and judgments without fully understanding the mechanism that powers those experiences. However, when it comes to ML, with the exception of neural networks, we can understand the underlying mechanisms that power certain determinations and classifications. We love the idea that ML can mirror our own ability to come to conclusions and that we can employ our critical thinking skills to make sure that process is as free from bias as possible.

The power of AI/ML allows us to automate repetitive, boring, uninspiring actions. We’d rather have content moderators, for instance...

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