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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

Trends and Insights across Industry

In the previous chapter, we looked at how the rising tide of artificial intelligence (AI) is affecting companies across the spectrum, as well as the ways AI is affecting companies within their own operations. In this chapter, we will look at the various ways we’re seeing AI trending across industries, through the lens of prominent and respected research organizations, in an effort to inspire product managers and entrepreneurs out there to begin to formulate their strategies by elevating their products into AI products. In addition, we will go over the various considerations they must keep in mind when attempting to approach AI, including AI readiness and enablement.

Analyzing trends and understanding the growth areas for AI and machine learning (ML) adoption can open us up to powerful future opportunities. The nature of what we build and how we work is changing because of this massive shift in adoption. For most of this book, we’...

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