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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

The professionals – examples of B2B products done right

The professionals are a good place to start with grouping products. AI will gravitate first toward use cases that can be profitable and allow for research and optimization. Because B2B products are products that are made for and used by other businesses, their use case is oriented completely toward the business world. This impacts everything from how they’re marketed to how they’re bought, sold, used, and negotiated. So many B2B products speak to the business impact that a product can satisfy for a customer across multiple levels. It’s a great way to learn potentially helpful applications of AI.

Part of the challenge with the rising supply of AI companies is that they need data to train on. Specifically, one of the ethical challenges with this expansion in data and AI products is being able to offer large amounts of data without leaking information that can identify you as a person, also known as...

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