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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 fourth industrial revolution – hospitals used to 
use candles

It’s hard to overstate the gravity of what AI adoption will mean for all industries and all job roles, and the delineation between technical and non-technical roles will start to change as well. Right now, AI is mentioned in business articles for the most part as a rising trend or wave, but this wave is quickly turning into a tsunami. In order to stay competitive with their peers, all companies across all industries will find themselves scrambling toward the digital transformation of AI. As more and more companies do this and successfully advance towards accomplishing AI adoption, we will also be seeing more demand for data-centric roles simply because most products, internal operations, and client discussions will evolve along with the AI adoption strategies of companies.

We’re also already starting to see automated ML (autoML) companies and offerings starting to grow as well. Companies...

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