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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

Micro AI – Feature level

It can be daunting to understand how various categories of AI fit together, and the reality is that in real-world AI product applications, many of these are working in concert. Seeing various examples of how that happens, particularly when we get to the later sections in this chapter, will offer us a way to see how much opportunity and potential AI really offers us!

We will consolidate ML, DL, computer vision, and NLP into their own section because these models are often used collaboratively as well. That collaboration can then bleed into the other subsets of AI. Robotics, expert systems, and fuzzy logic can remain in their own sections because their applications are so specialized in and of themselves. Seeing how subsets of AI can work together further results in the greater complexity and growth that powers innovation for our markets and brings to market products that serve, delight, and capture our hearts.

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