The AI Product Manager's Handbook: Develop a product that takes advantage of machine learning to solve AI problems

250 Pages·2023·7.314 MB·English

by  Irene Bratsis

The AI Product
Manager’s Handbook
Develop a product that takes advantage of machine learning
to solve AI problems
Irene Bratsis
BIRMINGHAM—MUMBAI
The AI Product Manager’s Handbook
Copyright © 2023 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted
in any form or by any means, without the prior written permission of the publisher, except in the case
of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information
presented. However, the information contained in this book is sold without warranty, either express
or implied. Neither the author(s), nor Packt Publishing or its dealers and distributors, will be held
liable for any damages caused or alleged to have been caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and
products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot
guarantee the accuracy of this information.
Publishing Product Manager: Dinesh Chaudhary
Senior Editor: Tazeen Shaikh
Technical Editor: Rahul Limbachiya
Copy Editor: Safis Editing
Project Coordinator: Farheen Fathima
Proofreader: Safis Editing
Indexer: Pratik Shirodkar
Production Designer: Alishon Mendonca
Marketing Coordinators: Shifa Ansari and Vinishka Kalra
First published: February 2023
Production reference: 2230223
Published by Packt Publishing Ltd.
Livery Place
35 Livery Street
Birmingham
B3 2PB, UK.
ISBN 978-1-80461-293-4
www.packtpub.com
For those courageous enough to believe they deserve their heart’s desires… evermore.
– Irene Bratsis
Contributors
About the author
Irene Bratsis is a director of digital product and data at the International WELL Building Institute
(IWBI). She has a bachelor's in economics, and after completing various MOOCs in data science and
big data analytics, she completed a data science program with Thinkful. Before joining IWBI, Irene
worked as an operations analyst at Tesla, a data scientist at Gesture, a data product manager at Beekin,
and head of product at Tenacity. Irene volunteers as NYC chapter co-lead for Women in Data, has
coordinated various AI accelerators, moderated countless events with a speaker series with Women
in AI called WaiTalk, and runs a monthly book club focused on data and AI books.
About the reviewer
Akshat Gurnani is a highly qualified individual with a background in the field of computer science
and machine learning. He has a master’s degree in computer science and a deep understanding of
various machine learning techniques and algorithms. He has experience working on various projects
related to natural language processing, computer vision, and deep learning. He has also published
several research papers in top-tier journals and conferences and has a proven track record in the field.
He has a passion for keeping up to date with the latest developments in their fields and has a strong
desire to continue learning and contributing to the field of artificial intelligence.

Table of Contents
Preface xiii
Part 1 – Lay of the Land – Terms, Infrastructure,
Types of AI, and Products Done Well
1
Understanding the Infrastructure and Tools for Building AI Products 3
Definitions – what is and is not AI 4 Data pipelines 14
ML versus DL – understanding the Managing projects – IaaS 15
difference 6
Deployment strategies – what do we
ML 6 do with these outputs? 16
DL 7
Shadow deployment strategy 17
Learning types in ML 8 A/B testing model deployment strategy 17
Supervised learning 8 Canary deployment strategy 17
Unsupervised learning 9 Succeeding in AI – how well-
Semi-supervised learning 10 managed AI companies do
Reinforcement learning 11 infrastructure right 18
The order – what is the optimal flow The promise of AI – where is AI
and where does every part of the taking us? 19
process live? 11 Summary 20
Step 1 – Data availability and centralization 11 Additional resources 20
Step 2 – Continuous maintenance 12
References 21
Database 13
Data warehouse 13
Data lake (and lakehouse) 14
viii Table of Contents
2
Model Development and Maintenance for AI Products 23
Understanding the stages of NPD 23 Training – when is a model ready for
market? 28
Step 1 – Discovery 24
Step 2 – Define 24 Deployment – what happens after the
Step 3 – Design 25 workstation? 32
Step 4 – Implementation 25 Testing and troubleshooting 34
Step 5 – Marketing 25
Refreshing – the ethics of how often
Step 6 – Training 26
we update our models 36
Step 7 – Launch 26
Summary 38
Model types – from linear regression
Additional resources 39
to neural networks 27
References 40
3
Machine Learning and Deep Learning Deep Dive 41
The old – exploring ML 42 and related tech 52
The new – exploring DL 43 Explainability – optimizing for
ethics, caveats, and responsibility 53
Invisible influences 44
A brief history of DL 45 Accuracy – optimizing for success 54
Types of neural networks 46 Summary 55
Emerging technologies – ancillary References 55
4
Commercializing AI Products 59
The professionals – examples of B2B The rebels – examples of red ocean
products done right 60 products 64
The artists – examples of B2C The GOAT – examples of
products done right 61 differentiated disruptive and
The pioneers – examples of blue dominant strategy products 65
ocean products 63 The dominant strategy 66
Table of Contents ix
The disruptive strategy 67 Summary 68
The differentiated strategy 67 References 69
5
AI Transformation and Its Impact on Product Management 71
Money and value – how AI could AI and nanotech across healthcare 79
revolutionize our economic systems 72 Basic needs – AI for Good 80
Goods and services – growth in Summary 81
commercial MVPs 74
Additional resources 82
Government and autonomy – how AI
References 82
will shape our borders and freedom 77
Sickness and health – the benefits of
Part 2 – Building an AI-Native Product
6
Understanding the AI-Native Product 87
Stages of AI product development 88 Customer success 95
Phase 1 – Ideation 88 Marketing/sales/go-to-market team 95
Phase 2 – Data management 89 Investing in your tech stack 95
Phase 3 – Research and development 90
Productizing AI-powered outputs
Phase 4 – Deployment 91
– how AI product management is
AI/ML product dream team 91 different 96
AI PM 92 AI customization 98
AI/ML/data strategists 92 Selling AI – product management as
Data engineer 92 a higher octave
Data analyst 93 of sales 99
Data scientist 93 Summary 100
ML engineer 93
References 101
Frontend/backend/full stack engineers 94
UX designers/researchers 94