Autorentext

Dr. Marily Nika is an award-winning GenAI Product Leader at Google and one of the world's foremost AI educators, with over 13 years of experience building AI products at Google and Meta. She holds a PhD in machine learning and is an author, TED AI speaker, Harvard Business School fellow and co-founder of the AI Product Hub (www.aiproduct.com) which offers AI product management certifications. Diego Granados is a Product Leader with more than 6 years of experience bringing AI products to life in top tech companies in Silicon Valley. He holds an MBA from Duke University and an M.S. in C.S. focused on AI & ML from Georgia Tech and is co-founder of the AI Product Hub (www.aiproduct.com) which offers AI product management certifications.

Klappentext

A comprehensive guide for aspiring and current AI product managers The AI Product Playbook: Strategies, Skills, and Frameworks for the AI-Driven Product Manager, by Dr. Marily Nika and Diego Granados, is a practical resource designed to empower product managers to effectively build, launch, and manage successful AI-powered products. This playbook bridges the gap between artificial intelligence theory and real-world product management, offering actionable learnings tailored to non-technical professionals. Drawing from extensive industry experience, Dr. Nika and Granados introduce the three essential AI product manager roles: AI Experiences PM, AI Builder PM, and AI-Enhanced PM. They offer guidance on developing skills crucial for each role and navigating common challenges in the workplace. Readers will also find valuable strategies for career growth, lifelong learning, and crafting a distinctive AI portfolio. Inside the book:

• Practical frameworks for discovering AI opportunities and aligning AI capabilities with business goals
• A deep technical dive with clear explanations of foundational AI and machine learning concepts, including supervised learning, unsupervised learning, reinforcement learning, and generative AI
• Guidelines for ethical AI implementation, addressing bias, fairness, and compliance with AI regulations
• Strategies for effective collaboration with cross-functional teams and enhancing productivity through AI

• Interactive exercises, action plans, checklists, templates, and quizzes designed to reinforce learning and build real-world skills Essential reading for aspiring and experienced product managers alike, The AI Product Playbook provides a roadmap to mastering AI-driven product management and advancing your career in the dynamic field of artificial intelligence.

  Inhalt

  Introduction xix

  Part I Foundational AI/ML Concepts 1

  Chapter 1 Artificial Intelligence and Machine Learning: What Every Product Manager Needs to Know 3

  AI vs. ml 4

  Why This Matters to a PM 4

  Key Differences Between AI and ml 5

  Common Misconceptions for PMs: Myths vs. Reality 7

  Your Glossary as a PM 7

  Grounding the Concepts: Real-World AI in Action 10

  The AI PM's Guiding Principles 14

  Chapter Summary and Key Takeaways 16

  Key Takeaways 16

  Onward: Peeking Under the Hood 17

  Chapter 2 How Machine Learning Models Learn: A Peek Under the Hood 19

  The Learning Process: Training, Validation, and Testing 20

  How Models Learn: An Example with k-Nearest Neighbors (k-NN) 22

  Applying k-NN (with k=1): 23

  Another Example: Testing an Unknown Fruit 26

  Evaluating Model Performance 27

  The Confusion Matrix: A Foundation for Understanding 27

  Key Classification Metrics (and Their PM Implications) 28

  The Precision-Recall Trade-Off 29

  Choosing the Right Metric 30

  Overfitting and Underfitting: Striking the Right Balance for Real-World Performance 31

  Overfitting: Memorizing Instead of Learning 31

  Underfitting: Missing the Forest for the Trees 32

  Visual Analogy: Fitting a Curve 32

  Finding the Sweet Spot: Generalization 33

  The PM's Role 33

  Human-in-the-Loop: Blending AI Power with Human Expertise 34

  What Is Human-in-the-Loop? 34

  Why HITL Is Essential for Product Managers (and Their Products) 35

  How to Implement HITL (PM Considerations) 37

  Chapter Summary and Key Takeaways 38

  Key Takeaways 39

  Onward: Understanding the Broader Process 39

  Chapter 3 The Big Picture: AI, ML, and You 41

  Understanding the Relationship Between AI, ML, and Product Goals 41

  Types of Machine Learning: Understanding the Spectrum of Learning 44

  Supervised Learning: Guiding the Model with Labeled Examples 46

  Technical Deep Dive: How Supervised Learning Models Learn from Labeled Data 48

  Critical Considerations for Product Managers 54

  Unsupervised Learning: Discovering Hidden Patterns in Your Data 55

  Technical Deep Dive: How Unsupervised Learning Models Discover Patterns 57

  Critical Considerations for Product Managers 60

  Reinforcement Learning: Learning Through Trial and Error 61

  Technical Deep Dive: How Reinforcement Learning Agents Learn Optimal Policies 63

  The Learning Process: Exploration, Exploitation, and Q-Learning 65

  Critical Considerations for Product Managers 67

  Generative AI: Powering a New Era of Language-Based Applications 67

  Technical Deep Dive: How LLMs Understand and Generate Language 69

  Critical Considerations for Product Managers 72

  The "Gotchas": A PM's Guide to LLM Limitations and Risks 73

  Navigating the Nuances of Generative AI: Understanding GenAI Evaluations- Ensuring Quality and Trust 75

  Prompt Engineering: The Art and Science of Talking to AI 84

  Types of Machine Learning: A Recap 89

  Introduction to Neural Networks and Deep Learning: The Engines of Complex Pattern Recognition 92

  Neural Networks: Mimicking the Brain's Connections (But Not Really) 92

  How Neural Networks Learn: Adjusting the Connections 94

  Technical Deep Dive: The Mechanics of Neural Networks and Deep Learning 95

  Challenges in Deep Learning 98

  Chapter Summary and Key Takeaways 99

  Key Takeaways 99

  Onward: Mapping the Process 100

  Chapter 4 The AI Lifecycle 101

  Problem Definition and Business Understanding: The "Why" 102

  Data Collection and Exploration: Understanding Your Ingredients 103

  Data Preprocessing: Preparing the Ingredients 104

  Feature Engineering: Crafting the Inputs for Success 104

  Model Selection and Training: Choosing the Right Algorithm 105

  Model Evaluation and Tuning: Ensuring Quality 106

  Model Deployment and Monitoring: Bringing AI to Life (and Keeping It Healthy) 107

  Retraining and Maintenance: Keeping Your Model Up-to-Date 108

  Chapter Summary and Key Takeaways 109

  Key Takeaways 109

  Onward: Exploring the AI PM Roles 110

  Part II AI PM Specializations 111

  Chapter 5 AI-Experiences PM: Shaping User Interaction with AI 113

  Key Responsibilities: Shaping the AI User Experience 114

  Day-to-Day Activities 117

  Required Skills and Knowledge: The AI-Experiences PM Toolkit 120

  Core Product Management Craft and Practices 120

  Engineering Foundations for PMs 121

  Essential Leadership and Collaboration Skills 122

  AI Lifecycle and Operational Awareness 123

  Illustrative Example: A Day in the Life of an AI-Experiences PM 124

  Challenges and Complexities 127

  How the AI-Experiences PM Interacts with Other Roles 129

  Chapter Summary and Key Takeaways 134

  Key Takeaways 134

  Onward: Architecting the AI Foundation 135

  Chapter 6 AI-Builder PM: Architecting the Foundation of Intelligent Systems 137

  Key Responsibilities: Building and Managing the AI Foundation 138

  Day-to-Day Activities 141

  Required Skills and Knowledge: The AI-Builder PM's Technical and Strategic Toolkit 144

  Core Product Management Craft and Practices 145

  Engineering Foundations for PMs 146

  Essential Leadership and Collaboration Skills 147

  AI Lifecycle and Operational Awareness 148

  Illustrative Example: A Day in the Life of an AI-Builder PM 149

  Challenges and Complexities 152

  How the AI-Builder PM Interacts with Other Roles 154

  Chapter Su…