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AI Product Manager's Handbook

You're reading from   AI Product Manager's Handbook Build, integrate, scale, and optimize products to grow as an AI product manager

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Product type Paperback
Published in Nov 2024
Publisher Packt
ISBN-13 9781835882849
Length 484 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 (26) Chapters Close

Preface 1. Part 1: Lay of the Land – Terms, Infrastructure, Types of AI, and Products Done Well
2. Understanding the Infrastructure and Tools for Building AI Products FREE CHAPTER 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. Other Books You May Enjoy
25. Index

Case study

In this case study, we will take a look at a fictional green tech company called GreenCo123, which specializes in optimizing energy consumption in commercial buildings. They’re working on developing the next generation of their energy management system, GridOS. This latest version will include AI features. The goal of this evolving product is to help businesses reduce energy costs, lower carbon emissions, and improve building sustainability by predicting energy demand and automating HVAC systems, lighting, and other energy-intensive operations.

GreenCo123 looked into the following features for bolstering GridOS with AI capabilities:

  • Energy demand forecasting: Time-series analysis and predictive modeling were done using LSTMs to handle time-dependent data used for predicting the energy demand of a building based on historical usage data, occupancy patterns, and operations schedules. This internal data was appended with external data like weather...
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