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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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Toc

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

Case study

In this section, we’re introducing a case study example that we’ll be discussing throughout Part 2 as a working example of how the concepts and principles are applied. In this chapter, we will be getting an introduction to a sample company and product, the way they organize their product organization and cross-functional teams, their tech stack, their productized outputs, how they’ve customized their verticals, and a brief overview of their GTM strategy.

Waterbear Inc. is building a flagship product, Akeira, to help tackle the mental health epidemic we’ve seen been exacerbated by the Covid-19 pandemic, specifically for women over the age of 18. What began as an idea to help people better understand themselves has now turned into a viable product people are using to get a better read on how their sentiments and moods are changing over time. The product uses NLP to scan journal entries that users type into the application, establish...

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