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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

Drawing from my own professional experiences, here is a list of actions I’ve taken at various points in my career so far:

  • Technical proficiency:
    • Technologist: Maintain articles on my LinkedIn and medium pages and take on speaking engagements to communicate my work. I also moderate events with Women in AI, giving a platform to other women doing incredible work in AI (and learning from them).
    • AI expert: Got various certifications and completed two boot camps. I buy Udemy courses occasionally to practice with different ML models outside of work.
    • Technical translator: Organized training with stakeholder teams to get them up to speed on mature data practices and I’ve done workshops explaining models used/alluded to when doing competitive analysis on other products.
    • Data steward: Documented data hygiene, data lineage, and data cataloging tools; I’ve also created data governance and access standards...
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