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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

Index

As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.

A

A/B testing 90

model deployment strategy 17

A/B testing tools 163

accuracy optimization 54, 55

Adaptive AI 188

AI adoption 178

potential risks, anticipating 179-181

AI adoption trends 188

autonomous AI development 191

creative AI 191

embedded AI 189, 190

ethical AI 190

general trends 188, 189

AI costs and pricing

managing 166

reference link 166

AI customization

verticals and customer groupings, consideration 98, 99

AI enablement 192, 193

AI Forum

reference link 105

AI Global Surveillance (AIGS) 78

AI/ML product dream team 91

AI/ML/data strategists 92

AI PM 92

customer success 95

data analyst 93

data engineer 92

data scientist 93

frontend/backend/full-stack engineers 94

marketing/sales/go-to-market team...

CONTINUE READING
83
Tech Concepts
36
Programming languages
73
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The AI Product Manager's Handbook
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