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The AI Product Manager's Handbook

You're reading from   The AI Product Manager's Handbook Develop a product that takes advantage of machine learning to solve AI problems

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Product type Paperback
Published in Feb 2023
Publisher Packt
ISBN-13 9781804612934
Length 250 pages
Edition 1st 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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Table of Contents (19) Chapters Close

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 FREE CHAPTER 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

Understanding the Infrastructure and Tools for Building AI Products

Laying a solid foundation is an essential part of understanding anything, and the frontier of artificial intelligence (AI) products seems a lot like our universe: ever-expanding. That rate of expansion is increasing with every passing year as we go deeper into a new way to conceptualize products, organizations, and the industries we’re all a part of. Virtually every aspect of our lives will be impacted in some way by AI and we hope those reading will come out of this experience more confident about what AI adoption will look like for the products they support or hope to build someday.

Part 1 of this book will serve as an overview of the lay of the land. We will cover terms, infrastructure, types of AI algorithms, and products done well, and by the end of this section, you will understand the various considerations when attempting to build an AI strategy, whether you’re looking to create a native-AI product or add AI features to an existing product.

Managing AI products is a highly iterative process, and the work of a product manager is to help your organization discover what the best combination of infrastructure, training, and deployment workflow is to maximize success in your target market. The performance and success of AI products lie in understanding the infrastructure needed for managing AI pipelines, the outputs of which will then be integrated into a product. In this chapter, we will cover everything from databases to workbenches to deployment strategies to tools you can use to manage your AI projects, as well as how to gauge your product’s efficacy.

This chapter will serve as a high-level overview of the subsequent chapters in Part 1 but it will foremost allow for a definition of terms, which are quite hard to come by in today’s marketing-heavy AI competitive landscape. These days, it feels like every product is an AI product, and marketing departments are trigger-happy with sprinkling that term around, rendering it almost useless as a descriptor. We suspect this won’t be changing anytime soon, but the more fluency consumers and customers alike have with the capabilities and specifics of AI, machine learning (ML), and data science, the more we should see clarity about how products are built and optimized. Understanding the context of AI is important for anyone considering building or supporting an AI product.

In this chapter, we will cover the following topics:

  • Definitions – what is and is not AI
  • ML versus DL – understanding the difference
  • Learning types in ML
  • The order – what is the optimal flow and where does every part of the process live?
  • DB 101 – databases, warehouses, data lakes, and lakehouses
  • Managing projects – IaaS
  • Deployment strategies – what do we do with these outputs?
  • Succeeding in AI – how well-managed AI companies do infrastructure right
  • The promise of AI – where is AI taking us?
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The AI Product Manager's Handbook
Published in: Feb 2023
Publisher: Packt
ISBN-13: 9781804612934
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