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The AI Product Manager's Handbook

You're reading from  The AI Product Manager's Handbook

Product type Book
Published in Feb 2023
Publisher Packt
ISBN-13 9781804612934
Pages 250 pages
Edition 1st Edition
Languages
Author (1):
Irene Bratsis Irene Bratsis
Profile icon Irene Bratsis

Table of Contents (19) Chapters

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 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

Fuzzy logic/fuzzy matching

Fuzzy matching, also referred to as approximate string matching, uses some logic to find terms or phrases that are similar to each other. Perhaps you’re looking through your database to isolate anyone with a first name John but some entries are Jonathan or Johnny. Fuzzy matching would be an intelligent way of finding those alternative names. Fuzzy matching was used ubiquitously in translation software before machine translation came into the picture. Whether you’re looking for alternative naming conventions or mistakes, fuzzy logic and matching are able to offer us intelligent ways for machines to find the things we’re looking for.

As with robotics and other areas of AI, we can see an ensemble with fuzzy matching as well. We’re seeing ML applied to fuzzy matching in an effort to improve accuracy. But even without ML, fuzzy logic and fuzzy matching can stand on their own as a subset of AI that’s been relied on heavily...

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