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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

Low-hanging fruit – quickest wins for AI enablement

By now, you’ve seen how involved applied ML is for an organization to embrace fully, and we’ve just spent most of this chapter looking through the various growth areas in AI for organizations that are already in business and are looking to capitalize on these growth areas. In Part 1 of this book, we discussed the various layers of infrastructure that need to be supported in an AI program. In Part 2 of this book, we discussed the AI-native product. In this current part of the book, we’re discussing the transition of incorporating AI into a traditional software product. This means we can now move on to set the stage for what this adoption looks like.

Before we get started, a caveat: we really can’t talk about AI transformation unless we also set ourselves up for success to be able to begin the long and arduous process that is AI adoption. We have to make sure the conditions are right. Whether...

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