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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

Building off our case study example from Chapter 6, in this chapter, we will be deepening our understanding of Waterbear and their flagship B2C product, Akeira, to better understand some of the principles outlined in this chapter and how they may appear in our working example. We will use the flowchart shown in Figure 7.1 to inform how we break down this chapter’s principles in our case study example:

Figure 7.2: Productizing basics for Akeira

Here are the steps involved:

  • Scope: Here, the product is a B2C mobile application that uses natural language processing (NLP) to digest the data users feed into a dashboard that is used to track progress toward their established goals. End users can expect that the dashboard will be refreshed regularly, every time they make a new submission into the app. Waterbear currently offers Akeira to women over the age of 18, but it’s in the process of creating different use cases for girls...
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