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The AI Product Manager's Handbook

You're reading from   The AI Product Manager's Handbook Develop a product that takes advantage of machine learning to solve AI problems

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Product type Paperback
Published in Feb 2023
Publisher Packt
ISBN-13 9781804612934
Length 250 pages
Edition 1st 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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Table of Contents (19) Chapters Close

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 FREE CHAPTER 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

Macro AI – Foundations and umbrellas

So far, we’ve talked a great deal about ML and DL models in the previous chapters. This was intentional because most of the time when we see AI advertised to us through various products, this is what the underlying technology employed is—for the most part. It’s either a DL or an ML algorithm that’s powering the products we’ve discussed. But as you’ve seen in the previous chapters, AI is a great umbrella term that can actually mean more than just an ML or a DL model is being used.

There are a number of major domains in AI that don’t involve ML and DL. We’ve minimally touched on the other areas but haven’t given them their due in their impact and contributions to the AI landscape. Focusing only on ML and DL makes sense from a practical perspective to offer AI technologists, entrepreneurs, and PMs the best chance to pitch their AI products to investors and users, but it also leaves...

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