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The AI Product Manager's Handbook

You're reading from  The AI Product Manager's Handbook

Product type Book
Published in Feb 2023
Publisher Packt
ISBN-13 9781804612934
Pages 250 pages
Edition 1st Edition
Languages
Author (1):
Irene Bratsis Irene Bratsis
Profile icon Irene Bratsis

Table of Contents (19) Chapters

Preface 1. Part 1 – Lay of the Land – Terms, Infrastructure, Types of AI, and Products Done Well
2. Chapter 1: Understanding the Infrastructure and Tools for Building AI Products 3. Chapter 2: Model Development and Maintenance for AI Products 4. Chapter 3: Machine Learning and Deep Learning Deep Dive 5. Chapter 4: Commercializing AI Products 6. Chapter 5: AI Transformation and Its Impact on Product Management 7. Part 2 – Building an AI-Native Product
8. Chapter 6: Understanding the AI-Native Product 9. Chapter 7: Productizing the ML Service 10. Chapter 8: Customization for Verticals, Customers, and Peer Groups 11. Chapter 9: Macro and Micro AI for Your Product 12. Chapter 10: Benchmarking Performance, Growth Hacking, and Cost 13. Part 3 – Integrating AI into Existing Non-AI Products
14. Chapter 11: The Rising Tide of AI 15. Chapter 12: Trends and Insights across Industry 16. Chapter 13: Evolving Products into AI Products 17. Index 18. Other Books You May Enjoy

Summary

In this chapter, we’ve covered a lot of ground. We’ve discussed the various areas of AI at a high level, giving us a macro landscape of the variety of options we can take when building AI products. We’ve also brought those options down to the feature level, giving us a micro view of applied AI features. We were then able to look at a few examples of collaborative AI products that have received positive feedback and acclaim, along with a few examples that highlight the challenges of AI products. Building AI products is still new. We’re still building out new use cases, and with every new AI product that comes to market, we’re able to discover new pathways to use these algorithms.

This means that every newly applied use case has the potential to show the world what AI can do, and that’s what makes the current phase we’re in so exciting. In order to uncover innovative new uses for AI/ML we must be willing to make mistakes and...

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