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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

Commercializing AI Products

Now that we’re in the period of artificial intelligence (AI) integration, we’re seeing many use cases of AI proliferating across industries. In our work managing AI products, we’ve certainly relied on AI consultants and PhD-level advisors to help us with modeling and orchestrating our data strategy to support a full-scale AI operation. However, as the rising tidal wave of AI continues to penetrate various companies and use cases, we’re seeing less of a reliance on breakthroughs achieved through advanced degrees. What’s most important now is familiarity with the use of even simple, reliable models. There’s a time and place for specialization. Data science and AI are massive umbrella terms but, based on my experiences as a product manager, I see a real need for data and AI generalists that can understand the use cases themselves and how they relate to a business perspective.

In the age of AI breakthroughs, we didn...

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The AI Product Manager's Handbook
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