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AI Product Manager's Handbook

You're reading from   AI Product Manager's Handbook The ultimate playbook to unlock AI product success with real-world insights and strategies

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Product type Paperback
Published in Nov 2024
Publisher Packt
ISBN-13 9781835882849
Length 488 pages
Edition 2nd 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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Toc

Table of Contents (27) Chapters Close

Preface 1. Part 1: Lay of the Land – Terms, Infrastructure, Types of AI, and Products Done Well FREE CHAPTER
2. Understanding the Infrastructure and Tools for Building AI Products 3. Model Development and Maintenance for AI Products 4. Deep Learning Deep Dive 5. Commercializing AI Products 6. AI Transformation and Its Impact on Product Management 7. Part 2: Building an AI-Native Product
8. Understanding the AI-Native Product 9. Productizing the ML Service 10. Customization for Verticals, Customers, and Peer Groups 11. Product Design for the AI-Native Product 12. Benchmarking Performance, Growth Hacking, and Cost 13. Managing the AI-Native Product 14. Part 3: Integrating AI into Existing Traditional Software Products
15. The Rising Tide of AI 16. Trends and Insights Across Industry 17. Evolving Products into AI Products 18. The Role of AI Product Design 19. Managing the Evolving AI Product 20. Part 4: Managing the AI PM Career
21. Starting a Career as an AI PM 22. What Does It Mean to Be a Good AI PM? 23. Maturing and Growing as an AI PM 24. Unlock Your Book’s Exclusive Benefits 25. Other Books You May Enjoy
26. Index

Case study

A financial services firm wants to update its credit scoring system because the existing model is based on traditional statistical models and is quickly becoming outdated. Given the influx of data and complexity in the market, their old system has become less effective. The firm is looking for a more sophisticated model that would handle the large volumes of data and the various data types and sources, and could also improve on predicting credit risk. If the new model could reduce the rate of defaults (primary goal) and improve creditworthy customer approval rates (secondary goal), the investment would be successful. Here’s why an MLP was chosen as the favorite among the DL models tested by the AI PM team:

  • MLPs can integrate easily with existing systems and handle complex, diverse datasets. So, it was a practical choice because it was integrated smoothly with the firm’s existing infrastructure.
  • MLPs excel at modeling non-linear interactions...
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