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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

The act of productizing involves taking a concept, a service, or a piece of technology and developing it into a commercial product that’s suitable for the customers you’re looking to attract. As we’ve seen throughout this chapter, this work isn’t just a matter of getting your product up and running and creating a landing page for your potential customers to magically find. Productizing involves critically understanding the business model you’re working in and the ultimate audience you’re building for. Remember that AI products can be thought of as AI/ML services that are being built into traditional software products. This means that another big part of productizing for AI products involves the standardization and ritualization of the AI/ML service in a way that’s repeatable and predictable for the internal operations teams as well as the customers that will come to rely on your product.

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