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

Cover Page....2

Table of Contents....3

Preface....5

Part 1 – Lay of the Land – Terms, Infrastructure, Types of AI, and Products Done Well....11

Chapter 1: Understanding the Infrastructure and Tools for Building AI Products....12

Definitions – what is and is not AI....13

ML versus DL – understanding the difference....16

Learning types in ML....19

The order – what is the optimal flow and where does every part of the process live?....24

Managing projects – IaaS....31

Deployment strategies – what do we do with these outputs?....32

Succeeding in AI – how well-managed AI companies do infrastructure right....34

The promise of AI – where is AI taking us?....36

Summary....38

Additional resources....39

References....40

Chapter 2: Model Development and Maintenance for AI Products....42

Understanding the stages of NPD....42

Model types – from linear regression to neural networks....47

Training – when is a model ready for market?....49

Deployment – what happens after the workstation?....54

Testing and troubleshooting....57

Refreshing – the ethics of how often we update our models....59

Summary....63

Additional resources....64

References....65

Chapter 3: Machine Learning and Deep Learning Deep Dive....67

The old – exploring ML....68

The new – exploring DL....69

Emerging technologies – ancillary and related tech....84

Explainability – optimizing for ethics, caveats, and responsibility....85

Accuracy – optimizing for success....87

Summary....88

References....89

Chapter 4: Commercializing AI Products....92

The professionals – examples of B2B products done right....93

The artists – examples of B2C products done right....95

The pioneers – examples of blue ocean products....98

The rebels – examples of red ocean products....100

The GOAT – examples of differentiated disruptive and dominant strategy products....102

Summary....107

References....107

Chapter 5: AI Transformation and Its Impact on Product Management....109

Money and value – how AI could revolutionize our economic systems....111

Goods and services – growth in commercial MVPs....114

Government and autonomy – how AI will shape our borders and freedom....117

Sickness and health – the benefits of AI and nanotech across healthcare....121

Basic needs – AI for Good....123

Summary....125

Additional resources....125

References....126

Part 2 – Building an AI-Native Product....130

Chapter 6: Understanding the AI-Native Product....131

Stages of AI product development....132

AI/ML product dream team....137

Investing in your tech stack....143

Productizing AI-powered outputs – how AI product management is different....145

AI customization....147

Selling AI – product management as a higher octave of sales....149

Summary....151

References....151

Chapter 7: Productizing the ML Service....153

Understanding the differences between AI and traditional software products....153

B2B versus B2C – productizing business models....164

Consistency and AIOps/MLOps – reliance and trust....169

Performance evaluation – testing, retraining, and hyperparameter tuning....170

Feedback loop – relationship building....172

Summary....173

References....174

Chapter 8: Customization for Verticals, Customers, and Peer Groups....175

Domains – orienting AI toward specific areas....176

Verticals – examination into four areas (FinTech, healthcare, consumer goods, and cybersecurity)....184

Anomaly detection and user and entity behavior analytics....190

Value metrics – evaluating performance across verticals and peer groups....191

Thought leadership – learning from peer groups....195

Summary....196

References....196

Chapter 9: Macro and Micro AI for Your Product....198

Macro AI – Foundations and umbrellas....199

ML....201

Robotics....206

Expert systems....208

Fuzzy logic/fuzzy matching....208

Micro AI – Feature level....209

ML (traditional/DL/computer vision/NLP)....210

Successes – Examples that inspire....214

Challenges – Common pitfalls....217

Summary....221

References....222

Chapter 10: Benchmarking Performance, Growth Hacking, and Cost....223

Value metrics – a guide to north star metrics, KPIs and OKRs....224

Hacking – product-led growth....234

The tech stack – early signals....237

Managing costs and pricing – AI is expensive....245

Summary....246

References....247

Part 3 – Integrating AI into Existing Non-AI Products....249

Chapter 11: The Rising Tide of AI....250

Evolve or die – when change is the only constant....251

The fourth industrial revolution – hospitals used to use candles....254

Fear is not the answer – there is more to gain than lose (or spend)....261

Summary....266

Chapter 12: Trends and Insights across Industry....267

Highest growth areas – Forrester, Gartner, and McKinsey research....268

Trends in AI adoption – let the data speak for itself....275

Low-hanging fruit – quickest wins for AI enablement....281

Summary....283

References....284

Chapter 13: Evolving Products into AI Products....286

Venn diagram – what’s possible and what’s probable....287

Data is king – the bloodstream of the company....293

Competition – love your enemies....299

Product strategy – building a blueprint that works for everyone....301

Red flags and green flags – what to look for and watch out for....308

Summary....311

Additional resources....312

Index....314

Why subscribe?....335

Other Books You May Enjoy....336

Packt is searching for authors like you....337

Share Your Thoughts....337

Download a free PDF copy of this book....338

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.

What you will learn

• Build AI products for the future using minimal resources
• Identify opportunities where AI can be leveraged to meet business needs
• Collaborate with cross-functional teams to develop and deploy AI products
• Analyze the benefits and costs of developing products using ML and DL
• Explore the role of ethics and responsibility in dealing with sensitive data
• Understand performance and efficacy across verticals

Who this book is for

This book is for product managers and other professionals interested in incorporating AI into their products. Foundational knowledge of AI is expected. If you understand the importance of AI as the rising fourth industrial revolution, this book will help you surf the tidal wave of digital transformation and change across industries.