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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

Testing and troubleshooting

In Chapter 1, we discussed the idea of continuous maintenance, which included:

  • Continuous integration
  • Continuous delivery
  • Continuous training
  • Continuous monitoring

This section will build on that and expand on how to test and troubleshoot issues related to ML products on an ongoing basis so that your product is set up for success. Once you’ve done your first deployment, we jump right into the continuous training and continuous maintenance portion of the continuous maintenance process we discussed in Chapter 1.

Remember, managing the performance of your models post-deployment is crucial and it will be a highly iterative, never-ending process of model maintenance. As is the case with traditional software development, you will continue to test, troubleshoot, and fix bugs for your AI/ML products as well. The only difference is that you will also screen for lags in performance and bugs related to your model.

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