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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

ML (traditional/DL/computer vision/NLP)

Which type of model you’re using will depend on your use case and goals for your product. As we’ve covered in varying chapters, the exact model you go with will depend on the data you have, how you’re able to tune your hyperparameters, and what level of explainability and transparency you’ll need for your use case. We’re focusing on AI/ML native products in this section of the book and, as such, identifying which ML model(s) you will use for the foundation of your product will be an important decision, and all features you add onto your core product will also be an act of doing a cost-benefit analysis of the models you’re adding to power those features.

Most products that are out there right now are not AI/ML native in that they are existing software programs and packages that are incrementally adding new AI features and then rebranding their products as AI products. This isn’t exactly true...

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