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The AI Product Manager's Handbook

The AI Product Manager's Handbook

By : Irene Bratsis
4.6 (28)
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The AI Product Manager's Handbook

The AI Product Manager's Handbook

4.6 (28)
By: Irene Bratsis

Overview of this book

Product managers working with artificial intelligence will be able to put their knowledge to work with this practical guide to applied AI. This book covers everything you need to know to drive product development and growth in the AI industry. From understanding AI and machine learning to developing and launching AI products, it provides the strategies, techniques, and tools you need to succeed. The first part of the book focuses on establishing a foundation of the concepts most relevant to maintaining AI pipelines. The next part focuses on building an AI-native product, and the final part guides you in integrating AI into existing products. You’ll learn about the types of AI, how to integrate AI into a product or business, and the infrastructure to support the exhaustive and ambitious endeavor of creating AI products or integrating AI into existing products. You’ll gain practical knowledge of managing AI product development processes, evaluating and optimizing AI models, and navigating complex ethical and legal considerations associated with AI products. With the help of real-world examples and case studies, you’ll stay ahead of the curve in the rapidly evolving field of AI and ML. By the end of this book, you’ll have understood how to navigate the world of AI from a product perspective.
Table of Contents (19 chapters)
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1
Part 1 – Lay of the Land – Terms, Infrastructure, Types of AI, and Products Done Well
7
Part 2 – Building an AI-Native Product
13
Part 3 – Integrating AI into Existing Non-AI Products

Understanding the AI-Native Product

In this chapter, we will go over the essential components for creating a strategy for building an AI product. This strategy will allow companies a process that will help them succeed in building an AI native tool. In Chapter 2, we briefly introduced the new product development stages, and in this chapter, we will build on that structure by focusing on the most important phases of introducing a new AI/ML native product: ideation, data management, research, development, and deployment. We will also address the main contributors to your AI/ML product team, as well as the tech stack that will empower them. AI native products begin and end with your data, and the roles you fill to support the team responsible for their creation will be critical to their success. When building an AI native tool, do your due diligence so that you aren’t being wasteful of your company’s finances and resources. A bad hire or incorrect tech investment can be...

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