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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

Robotics

If AI is considered an umbrella term for machines mimicking how humans might work and reach conclusions, this is perhaps most visibly obvious with robotics, which is physically trying to reproduce the work of a human. The term robot is in and of itself a generic term that encompasses a lot of nuances, much like the term AI. ML is considered as powerful as it is because of the ability of machines to learn from past actions and behaviors. In that sense, perhaps they are considered more advanced than the underlying tech that powers robotics, but we felt it wasn’t fair to exclude robotics from AI because if a robot can make a meal, make a part for a car, or assist with surgery, it’s intelligent enough to be considered in the realm of AI.

The heart of innovations in this space will also come from ML being used in combination with robotics so that it can learn from the past. In Chapter 3, we briefly touched on the idea of Boston Dynamics robot dogs not using ML...

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