{"product_id":"the-ai-product-playbook-strategies-skills-and-frameworks-for-the-ai-driven-product-manager-paperback-softback-9781394335657","title":"The AI Product Playbook; Strategies, Skills, and Frameworks for the AI-Driven Product Manager (Paperback \/ softback) 9781394335657","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eThe AI Product Playbook\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eStrategies, Skills, and Frameworks for the AI-Driven Product Manager\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eMarily Nika (Author), Diego Granados (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781394335657, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePaperback \/ softback, published 9 October 2025\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e336 pages\u003cbr\u003e22.6 x 15.2 x 2 cm, 0.454 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eA comprehensive guide for aspiring and current AI product managers\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003e\u003ci\u003eThe AI Product Playbook: Strategies, Skills, and Frameworks for the AI-Driven Product Manager,\u003c\/i\u003e 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. \u003c\/p\u003e\n\u003cp\u003eDrawing 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. \u003c\/p\u003e\n\u003cp\u003eInside the book: \u003c\/p\u003e\n\u003cul\u003e \u003cli\u003ePractical frameworks for discovering AI opportunities and aligning AI capabilities with business goals\u003c\/li\u003e \u003cli\u003eA deep technical dive with clear explanations of foundational AI and machine learning concepts, including supervised learning, unsupervised learning, reinforcement learning, and generative AI\u003c\/li\u003e \u003cli\u003eGuidelines for ethical AI implementation, addressing bias, fairness, and compliance with AI regulations\u003c\/li\u003e \u003cli\u003eStrategies for effective collaboration with cross-functional teams and enhancing productivity through AI\u003c\/li\u003e \u003cli\u003eInteractive exercises, action plans, checklists, templates, and quizzes designed to reinforce learning and build real-world skills\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eEssential reading for aspiring and experienced product managers alike, \u003ci\u003eThe AI Product Playbook\u003c\/i\u003e provides a roadmap to mastering AI-driven product management and advancing your career in the dynamic field of artificial intelligence.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eIntroduction xix\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I Foundational AI\/ML Concepts 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 Artificial Intelligence and Machine Learning: What Every Product Manager Needs to Know 3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAI vs. ml 4\u003c\/p\u003e \u003cp\u003eWhy This Matters to a PM 4\u003c\/p\u003e \u003cp\u003eKey Differences Between AI and ml 5\u003c\/p\u003e \u003cp\u003eCommon Misconceptions for PMs: Myths vs. Reality 7\u003c\/p\u003e \u003cp\u003eYour Glossary as a PM 7\u003c\/p\u003e \u003cp\u003eGrounding the Concepts: Real-World AI in Action 10\u003c\/p\u003e \u003cp\u003eThe AI PM’s Guiding Principles 14\u003c\/p\u003e \u003cp\u003eChapter Summary and Key Takeaways 16\u003c\/p\u003e \u003cp\u003eKey Takeaways 16\u003c\/p\u003e \u003cp\u003eOnward: Peeking Under the Hood 17\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 How Machine Learning Models Learn: A Peek Under the Hood 19\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Learning Process: Training, Validation, and Testing 20\u003c\/p\u003e \u003cp\u003eHow Models Learn: An Example with k-Nearest Neighbors (k-NN) 22\u003c\/p\u003e \u003cp\u003eApplying k-NN (with k=1): 23\u003c\/p\u003e \u003cp\u003eAnother Example: Testing an Unknown Fruit 26\u003c\/p\u003e \u003cp\u003eEvaluating Model Performance 27\u003c\/p\u003e \u003cp\u003eThe Confusion Matrix: A Foundation for Understanding 27\u003c\/p\u003e \u003cp\u003eKey Classification Metrics (and Their PM Implications) 28\u003c\/p\u003e \u003cp\u003eThe Precision-Recall Trade-Off 29\u003c\/p\u003e \u003cp\u003eChoosing the Right Metric 30\u003c\/p\u003e \u003cp\u003eOverfitting and Underfitting: Striking the Right Balance for Real-World Performance 31\u003c\/p\u003e \u003cp\u003eOverfitting: Memorizing Instead of Learning 31\u003c\/p\u003e \u003cp\u003eUnderfitting: Missing the Forest for the Trees 32\u003c\/p\u003e \u003cp\u003eVisual Analogy: Fitting a Curve 32\u003c\/p\u003e \u003cp\u003eFinding the Sweet Spot: Generalization 33\u003c\/p\u003e \u003cp\u003eThe PM’s Role 33\u003c\/p\u003e \u003cp\u003eHuman-in-the-Loop: Blending AI Power with Human Expertise 34\u003c\/p\u003e \u003cp\u003eWhat Is Human-in-the-Loop? 34\u003c\/p\u003e \u003cp\u003eWhy HITL Is Essential for Product Managers (and Their Products) 35\u003c\/p\u003e \u003cp\u003eHow to Implement HITL (PM Considerations) 37\u003c\/p\u003e \u003cp\u003eChapter Summary and Key Takeaways 38\u003c\/p\u003e \u003cp\u003eKey Takeaways 39\u003c\/p\u003e \u003cp\u003eOnward: Understanding the Broader Process 39\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 The Big Picture: AI, ML, and You 41\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUnderstanding the Relationship Between AI, ML, and Product Goals 41\u003c\/p\u003e \u003cp\u003eTypes of Machine Learning: Understanding the Spectrum of Learning 44\u003c\/p\u003e \u003cp\u003eSupervised Learning: Guiding the Model with Labeled Examples 46\u003c\/p\u003e \u003cp\u003eTechnical Deep Dive: How Supervised Learning Models Learn from Labeled Data 48\u003c\/p\u003e \u003cp\u003eCritical Considerations for Product Managers 54\u003c\/p\u003e \u003cp\u003eUnsupervised Learning: Discovering Hidden Patterns in Your Data 55\u003c\/p\u003e \u003cp\u003eTechnical Deep Dive: How Unsupervised Learning Models Discover Patterns 57\u003c\/p\u003e \u003cp\u003eCritical Considerations for Product Managers 60\u003c\/p\u003e \u003cp\u003eReinforcement Learning: Learning Through Trial and Error 61\u003c\/p\u003e \u003cp\u003eTechnical Deep Dive: How Reinforcement Learning Agents Learn Optimal Policies 63\u003c\/p\u003e \u003cp\u003eThe Learning Process: Exploration, Exploitation, and Q-Learning 65\u003c\/p\u003e \u003cp\u003eCritical Considerations for Product Managers 67\u003c\/p\u003e \u003cp\u003eGenerative AI: Powering a New Era of Language-Based Applications 67\u003c\/p\u003e \u003cp\u003eTechnical Deep Dive: How LLMs Understand and Generate Language 69\u003c\/p\u003e \u003cp\u003eCritical Considerations for Product Managers 72\u003c\/p\u003e \u003cp\u003eThe “Gotchas”: A PM’s Guide to LLM Limitations and Risks 73\u003c\/p\u003e \u003cp\u003eNavigating the Nuances of Generative AI: Understanding GenAI Evaluations— Ensuring Quality and Trust 75\u003c\/p\u003e \u003cp\u003ePrompt Engineering: The Art and Science of Talking to AI 84\u003c\/p\u003e \u003cp\u003eTypes of Machine Learning: A Recap 89\u003c\/p\u003e \u003cp\u003eIntroduction to Neural Networks and Deep Learning: The Engines of Complex Pattern Recognition 92\u003c\/p\u003e \u003cp\u003eNeural Networks: Mimicking the Brain’s Connections (But Not Really) 92\u003c\/p\u003e \u003cp\u003eHow Neural Networks Learn: Adjusting the Connections 94\u003c\/p\u003e \u003cp\u003eTechnical Deep Dive: The Mechanics of Neural Networks and Deep Learning 95\u003c\/p\u003e \u003cp\u003eChallenges in Deep Learning 98\u003c\/p\u003e \u003cp\u003eChapter Summary and Key Takeaways 99\u003c\/p\u003e \u003cp\u003eKey Takeaways 99\u003c\/p\u003e \u003cp\u003eOnward: Mapping the Process 100\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 The AI Lifecycle 101\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eProblem Definition and Business Understanding: The “Why” 102\u003c\/p\u003e \u003cp\u003eData Collection and Exploration: Understanding Your Ingredients 103\u003c\/p\u003e \u003cp\u003eData Preprocessing: Preparing the Ingredients 104\u003c\/p\u003e \u003cp\u003eFeature Engineering: Crafting the Inputs for Success 104\u003c\/p\u003e \u003cp\u003eModel Selection and Training: Choosing the Right Algorithm 105\u003c\/p\u003e \u003cp\u003eModel Evaluation and Tuning: Ensuring Quality 106\u003c\/p\u003e \u003cp\u003eModel Deployment and Monitoring: Bringing AI to Life (and Keeping It Healthy) 107\u003c\/p\u003e \u003cp\u003eRetraining and Maintenance: Keeping Your Model Up-to-Date 108\u003c\/p\u003e \u003cp\u003eChapter Summary and Key Takeaways 109\u003c\/p\u003e \u003cp\u003eKey Takeaways 109\u003c\/p\u003e \u003cp\u003eOnward: Exploring the AI PM Roles 110\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II AI PM Specializations 111\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 AI-Experiences PM: Shaping User Interaction with AI 113\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eKey Responsibilities: Shaping the AI User Experience 114\u003c\/p\u003e \u003cp\u003eDay-to-Day Activities 117\u003c\/p\u003e \u003cp\u003eRequired Skills and Knowledge: The AI-Experiences PM Toolkit 120\u003c\/p\u003e \u003cp\u003eCore Product Management Craft and Practices 120\u003c\/p\u003e \u003cp\u003eEngineering Foundations for PMs 121\u003c\/p\u003e \u003cp\u003eEssential Leadership and Collaboration Skills 122\u003c\/p\u003e \u003cp\u003eAI Lifecycle and Operational Awareness 123\u003c\/p\u003e \u003cp\u003eIllustrative Example: A Day in the Life of an AI-Experiences PM 124\u003c\/p\u003e \u003cp\u003eChallenges and Complexities 127\u003c\/p\u003e \u003cp\u003eHow the AI-Experiences PM Interacts with Other Roles 129\u003c\/p\u003e \u003cp\u003eChapter Summary and Key Takeaways 134\u003c\/p\u003e \u003cp\u003eKey Takeaways 134\u003c\/p\u003e \u003cp\u003eOnward: Architecting the AI Foundation 135\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 AI-Builder PM: Architecting the Foundation of Intelligent Systems 137\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eKey Responsibilities: Building and Managing the AI Foundation 138\u003c\/p\u003e \u003cp\u003eDay-to-Day Activities 141\u003c\/p\u003e \u003cp\u003eRequired Skills and Knowledge: The AI-Builder PM’s Technical and Strategic Toolkit 144\u003c\/p\u003e \u003cp\u003eCore Product Management Craft and Practices 145\u003c\/p\u003e \u003cp\u003eEngineering Foundations for PMs 146\u003c\/p\u003e \u003cp\u003eEssential Leadership and Collaboration Skills 147\u003c\/p\u003e \u003cp\u003eAI Lifecycle and Operational Awareness 148\u003c\/p\u003e \u003cp\u003eIllustrative Example: A Day in the Life of an AI-Builder PM 149\u003c\/p\u003e \u003cp\u003eChallenges and Complexities 152\u003c\/p\u003e \u003cp\u003eHow the AI-Builder PM Interacts with Other Roles 154\u003c\/p\u003e \u003cp\u003eChapter Summary and Key Takeaways 156\u003c\/p\u003e \u003cp\u003eKey Takeaways 157\u003c\/p\u003e \u003cp\u003eOnward: Supercharging the PM Workflow 158\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 AI-Enhanced PM: Supercharging Product Management with AI 159\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eKey Responsibilities: Augmenting PM Workflows and Decision-Making with AI 160\u003c\/p\u003e \u003cp\u003eDay-to-Day Activities 162\u003c\/p\u003e \u003cp\u003eRequired Skills and Knowledge: The AI-Enhanced PM’s Toolkit 165\u003c\/p\u003e \u003cp\u003eCore Product Management Craft and Practices 165\u003c\/p\u003e \u003cp\u003eEngineering Foundations for PMs 166\u003c\/p\u003e \u003cp\u003eEssential Leadership and Collaboration Skills 167\u003c\/p\u003e \u003cp\u003eAI Lifecycle and Operational Awareness 168\u003c\/p\u003e \u003cp\u003eIllustrative Example: A Day in the Life of an AI-Enhanced PM 169\u003c\/p\u003e \u003cp\u003eExamples of AI Tools 172\u003c\/p\u003e \u003cp\u003eChallenges and Complexities 173\u003c\/p\u003e \u003cp\u003eHow the AI-Enhanced PM Interacts with Other Roles 175\u003c\/p\u003e \u003cp\u003eSkill Comparison: AI-Experiences PM, AI-Builder PM, and AI-Enhanced PM 177\u003c\/p\u003e \u003cp\u003eChapter Summary and Key Takeaways 184\u003c\/p\u003e \u003cp\u003eKey Takeaways 185\u003c\/p\u003e \u003cp\u003eOnward: From Theory to Action 185\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III Connecting the Dots Between AI\/ML Knowledge and PM Craft 187\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 Identifying and Evaluating AI Opportunities 189\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUncovering Potential Use Cases—Mining Your Product for AI Gold 189\u003c\/p\u003e \u003cp\u003eRecognizing Data-Rich Problem Areas 190\u003c\/p\u003e \u003cp\u003eAnalyzing Existing Data Sources 192\u003c\/p\u003e \u003cp\u003eAsking the Right Questions 193\u003c\/p\u003e \u003cp\u003eAI\/ML Capability Matching: Connecting Problems to Solutions 194\u003c\/p\u003e \u003cp\u003eUnderstanding Your AI\/ML Toolkit: Key Capabilities 195\u003c\/p\u003e \u003cp\u003eMatching Capabilities to Problems: A Practical Approach 200\u003c\/p\u003e \u003cp\u003eFeature: Search Functionality in a Document Management System 200\u003c\/p\u003e \u003cp\u003eFeature: Customer Support Chatbot 201\u003c\/p\u003e \u003cp\u003eFeature: Reporting Dashboard for Marketing Campaigns 201\u003c\/p\u003e \u003cp\u003eFinding AI Opportunities in the User Journey 202\u003c\/p\u003e \u003cp\u003eMapping the User Journey: Charting the Course 202\u003c\/p\u003e \u003cp\u003eIdentifying Pain Points and Opportunities: The AI Detective Work 204\u003c\/p\u003e \u003cp\u003eApplying AI\/ML to Enhance Touchpoints: The Transformation 205\u003c\/p\u003e \u003cp\u003eFeature Enhancement Through AI\/ML— Transforming Existing Functionality 208\u003c\/p\u003e \u003cp\u003eIdentifying Enhancement Opportunities: Finding the Weak Spots 209\u003c\/p\u003e \u003cp\u003eApplying AI\/ML to Enhance Features: The Transformation Process 210\u003c\/p\u003e \u003cp\u003eFeature: Standard Search Functionality 212\u003c\/p\u003e \u003cp\u003eFeature: Data Entry Form 212\u003c\/p\u003e \u003cp\u003eFeature: Reporting Dashboard 212\u003c\/p\u003e \u003cp\u003eProactive Product Management—Anticipating User Needs with AI 213\u003c\/p\u003e \u003cp\u003eUnderstanding the Power of Prediction and Automation 213\u003c\/p\u003e \u003cp\u003eKey Areas for Predictive and Automation Opportunities 214\u003c\/p\u003e \u003cp\u003eIdentifying Opportunities: A Practical Approach 216\u003c\/p\u003e \u003cp\u003eResponsible AI Foundations—Ethical and Feasibility Considerations 217\u003c\/p\u003e \u003cp\u003eEthical Considerations: The “Do No Harm” Principle 217\u003c\/p\u003e \u003cp\u003eFeasibility Considerations: Can We Actually Build This? 220\u003c\/p\u003e \u003cp\u003ePractical Ideation Techniques for AI\/ML Use Cases—Thinking Like an AI-First Product Manager 221\u003c\/p\u003e \u003cp\u003eIdeation Techniques: Unleashing Your AI Creativity 222\u003c\/p\u003e \u003cp\u003e“AI Feature Storming”: The Brain Dump 222\u003c\/p\u003e \u003cp\u003e“AI Scenario Planning”: Walking in the User’s Shoes 223\u003c\/p\u003e \u003cp\u003e“Data Opportunity Mapping”: Leveraging Your Data Assets 223\u003c\/p\u003e \u003cp\u003e“AI Capability Alignment”: The Matching Game 224\u003c\/p\u003e \u003cp\u003e“AI-Powered Feature Reverse Engineering”: Learning from Others 225\u003c\/p\u003e \u003cp\u003eCultivating an AI-First Mindset 226\u003c\/p\u003e \u003cp\u003eChapter Summary and Key Takeaways 226\u003c\/p\u003e \u003cp\u003eKey Takeaways 227\u003c\/p\u003e \u003cp\u003eOnward: Measuring the Value of Your Ideas 227\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 ROI Calculation for AI Projects: Measuring the Impact and Demonstrating Value 229\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eFrom Model Performance to Business Impact: A PM’s Guide to AI Metrics 229\u003c\/p\u003e \u003cp\u003eDefining AI\/ML-Specific Metrics: The Foundation for Measuring ROI 230\u003c\/p\u003e \u003cp\u003eThe Importance of Baselines: Knowing Where You Started 230\u003c\/p\u003e \u003cp\u003eUnderstanding the Confusion Matrix: Decoding Classification Performance 231\u003c\/p\u003e \u003cp\u003eKey Performance Metrics for AI\/ML Models: Beyond the Confusion Matrix 233\u003c\/p\u003e \u003cp\u003eContext Matters: Selecting the Right Metrics for Your AI\/ML Application 237\u003c\/p\u003e \u003cp\u003e1. Define Your Business Goals (and Connect Them to User Needs) 237\u003c\/p\u003e \u003cp\u003e2. Consider the Type of AI\/ML Application (and Its Inherent Trade-Offs) 238\u003c\/p\u003e \u003cp\u003e3. Evaluate the Cost of Errors: The Risk Assessment 239\u003c\/p\u003e \u003cp\u003e4. Translate Technical Metrics into Business Impact 240\u003c\/p\u003e \u003cp\u003eImportant Considerations 240\u003c\/p\u003e \u003cp\u003eEnd-to-End Example—Predicting Churn in a Subscription Service 241\u003c\/p\u003e \u003cp\u003e1. Identify the Business Goal: Defining the “Why” 241\u003c\/p\u003e \u003cp\u003e2. Define the AI\/ML Application and Solution 242\u003c\/p\u003e \u003cp\u003e3. Identify Data Sources and Engineer Features: The Raw Materials 243\u003c\/p\u003e \u003cp\u003eAvailable Data 243\u003c\/p\u003e \u003cp\u003eFeature Engineering 243\u003c\/p\u003e \u003cp\u003eThe Product Manager’s Role in This Stage 244\u003c\/p\u003e \u003cp\u003e4. Select the Metrics: Defining Success 245\u003c\/p\u003e \u003cp\u003eThe Cost of Errors: Prioritizing What Matters 245\u003c\/p\u003e \u003cp\u003eOur Chosen Metrics 246\u003c\/p\u003e \u003cp\u003e5. Establish Baseline Metrics: Setting the Starting Point 246\u003c\/p\u003e \u003cp\u003e6. Conduct Model Training and Evaluation: Building and Testing the AI 247\u003c\/p\u003e \u003cp\u003e7. Conduct A\/B Testing: Measuring Real-World Impact 248\u003c\/p\u003e \u003cp\u003e8. Calculate the Results and ROI: Quantifying the Value 248\u003c\/p\u003e \u003cp\u003eTranslating Results into Business Impact 249\u003c\/p\u003e \u003cp\u003eMonitoring for Long-Term Success 249\u003c\/p\u003e \u003cp\u003e9. Monitor and Maintain the Model for Long-Term Success 250\u003c\/p\u003e \u003cp\u003eA\/B Testing for AI and ML Projects: Validating Impact and Optimizing Performance 251\u003c\/p\u003e \u003cp\u003eWhat Is A\/B Testing (in a Nutshell)? 251\u003c\/p\u003e \u003cp\u003eWhy Is A\/B Testing Especially Important for AI\/ML? 252\u003c\/p\u003e \u003cp\u003eHow to Conduct A\/B Testing for AI and ML: A Step-by-Step Guide 253\u003c\/p\u003e \u003cp\u003eKey Considerations for AI\/ML A\/B Testing 258\u003c\/p\u003e \u003cp\u003eChapter Summary and Key Takeaways 259\u003c\/p\u003e \u003cp\u003eKey Takeaways 259\u003c\/p\u003e \u003cp\u003eOnward: From the Lab to a Live Product 260\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10 Building and Deploying AI Solutions: From Lab to Live 261\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eMLOps: The Key to Reliable and Scalable AI 261\u003c\/p\u003e \u003cp\u003eKey Components of MLOps—The AI Production Line 264\u003c\/p\u003e \u003cp\u003eCI\/CD, IaC, and Collaboration: The Foundational Pillars of MLOps 271\u003c\/p\u003e \u003cp\u003eGlossary of Key MLOps Terms 272\u003c\/p\u003e \u003cp\u003eMLOps End-to-End Example: Churn Prediction in a Subscription Service (Product Manager’s Perspective) 274\u003c\/p\u003e \u003cp\u003eChapter Summary and Key Takeaways 278\u003c\/p\u003e \u003cp\u003eKey Takeaways 278\u003c\/p\u003e \u003cp\u003eOnward: Building with Integrity 279\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11 Responsible AI and Ethical Considerations: Building AI with Integrity 281\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUnderstanding AI Bias and Fairness: The Foundation of Responsible AI 281\u003c\/p\u003e \u003cp\u003eIdentifying Potential Biases: Where Bias Can Creep In 282\u003c\/p\u003e \u003cp\u003eMitigating Potential Biases: A Proactive Approach 285\u003c\/p\u003e \u003cp\u003eProtected Classes and AI Fairness— Designing for Inclusion 287\u003c\/p\u003e \u003cp\u003eWhat are Protected Classes? 287\u003c\/p\u003e \u003cp\u003eWhy Focus on Protected Classes? (The Legal and Ethical Imperative) 287\u003c\/p\u003e \u003cp\u003eHow Protected Classes Relate to AI Bias: The Mechanisms of Discrimination 288\u003c\/p\u003e \u003cp\u003eMitigating Bias Related to Protected Classes: Actionable Steps for PMs 289\u003c\/p\u003e \u003cp\u003eAI Ethics and Legal Compliance—From Principles to Practice 291\u003c\/p\u003e \u003cp\u003eUnderstanding the Ethical Landscape: Core Principles 291\u003c\/p\u003e \u003cp\u003eUnderstanding the Legal Landscape: Key Regulations 292\u003c\/p\u003e \u003cp\u003eActionable Steps for Product Managers: Building Ethically and Legally Compliant AI 293\u003c\/p\u003e \u003cp\u003eEngaging with the Community and External Stakeholders 297\u003c\/p\u003e \u003cp\u003eChapter Summary and Key Takeaways 298\u003c\/p\u003e \u003cp\u003eKey Takeaways 298\u003c\/p\u003e \u003cp\u003eOnward: Paving Your Path 299\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 12 Conclusion: Paving Your Own Path to AI PM 301\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eEmbrace Lifelong Learning: Stay Curious and Iterative 302\u003c\/p\u003e \u003cp\u003eCultivate a User-Centric AI Mindset 303\u003c\/p\u003e \u003cp\u003eDeepen Cross-Functional Collaboration Skills 303\u003c\/p\u003e \u003cp\u003eBuild a Distinct AI Portfolio (Show, Don’t Just Tell) 304\u003c\/p\u003e \u003cp\u003eDevelop a Personal Vision for Your AI Career 305\u003c\/p\u003e \u003cp\u003eKeep Resilience and Adaptability at the Core 305\u003c\/p\u003e \u003cp\u003eFinal Thoughts 306\u003c\/p\u003e \u003cp\u003eIndex 307\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Computer science [\u003ca title=\"See our other books on Computer science\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Computer%20science%20%5BUY%5D%22\"\u003eUY\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley","offers":[{"title":"Brand New","offer_id":52453517000984,"sku":"9781394335657","price":18.87,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781394335657.jpg?v=1785285586","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/the-ai-product-playbook-strategies-skills-and-frameworks-for-the-ai-driven-product-manager-paperback-softback-9781394335657","provider":"Freshly Printed Books","version":"1.0","type":"link"}