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[null,"1.","Record Nr.",null,"UNINA9911031643503321"]
[null,null,"Autore",null,"Nika Marily"]
[null,null,"Titolo",null,"The AI product playbook : strategies, skills, and frameworks for the AI-driven product manager / / Marily Nika, Diego Granados"]
[null,null,"Pubbl/distr/stampa",null,"Newark : , : John Wiley & Sons, Incorporated, , [2026]"]
[{"$nulls":4},"©2026"]
[null,null,"ISBN",null,"1394352468"]
[{"$nulls":4},"1394335660"]
[null,null,"Descrizione fisica",null,"1 online resource (339 pages)"]
[null,null,"Disciplina",null,"658.50028563"]
[null,null,"Soggetti",null,"Product management"]
[{"$nulls":4},"Artificial intelligence"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Introduction -- Part I Foundational AI/ML Concepts -- Chapter 1 Artificial Intelligence and Machine Learning: What Every Product Manager Needs to Know -- AI vs. ML -- Why This Matters to a PM -- Key Differences Between AI and ML -- Common Misconceptions for PMs: Myths vs. Reality -- Your Glossary as a PM -- Grounding the Concepts: Real-World AI in Action -- The AI PM's Guiding Principles -- Chapter Summary and Key Takeaways -- Key Takeaways -- Onward: Peeking Under the Hood -- Chapter 2 How Machine Learning Models Learn: A Peek Under the Hood -- The Learning Process: Training, Validation, and Testing -- How Models Learn: An Example with k-Nearest Neighbors (k-NN) -- Applying k-NN (with k=1): -- Another Example: Testing an Unknown Fruit -- Evaluating Model Performance -- The Confusion Matrix: A Foundation for Understanding -- Key Classification Metrics (and Their PM Implications) -- The Precision-Recall Trade-Off -- Choosing the Right Metric -- Overfitting and Underfitting: Striking the Right Balance for Real-World Performance -- Overfitting: Memorizing Instead of Learning -- Underfitting: Missing the Forest for the Trees -- Visual Analogy: Fitting a Curve -- Finding the Sweet Spot: Generalization -- The PM's Role -- Human-in-the-Loop: Blending AI Power with Human Expertise -- What Is Human-in-the-Loop? -- Why HITL Is Essential for Product Managers (and Their Products) -- How to Implement HITL (PM Considerations) -- Chapter Summary and Key Takeaways -- Key Takeaways -- Onward: "]
[{"$nulls":4},"Understanding the Broader Process -- Chapter 3 The Big Picture: AI, ML, and You -- Understanding the Relationship Between AI, ML, and Product Goals -- Types of Machine Learning: Understanding the Spectrum of Learning."]
[{"$nulls":4},"Supervised Learning: Guiding the Model with Labeled Examples -- Unsupervised Learning: Discovering Hidden Patterns in Your Data -- Reinforcement Learning: Learning Through Trial and Error -- Generative AI: Powering a New Era of Language-Based Applications -- The \"Gotchas\": A PM's Guide to LLM Limitations and Risks -- Types of Machine Learning: A Recap -- Introduction to Neural Networks and Deep Learning: The Engines of Complex Pattern Recognition -- Neural Networks: Mimicking the Brain's Connections (But Not Really) -- How Neural Networks Learn: Adjusting the Connections -- Technical Deep Dive: The Mechanics of Neural Networks and Deep Learning -- Challenges in Deep Learning -- Chapter Summary and Key Takeaways -- Key Takeaways -- Onward: Mapping the Process -- Chapter 4 The AI Lifecycle -- Problem Definition and Business Understanding: The \"Why\" -- Data Collection and Exploration: Understanding Your Ingredients -- Data Preprocessing: Preparing the Ingredients -- Feature Engineering: Crafting the Inputs for Success -- Model Selection and Training: Choosing the Right Algorithm -- Model Evaluation and Tuning: Ensuring Quality -- Model Deployment and Monitoring: Bringing AI to Life (and Keeping It Healthy) -- Retraining and Maintenance: Keeping Your Model Up-to-Date -- Chapter Summary and Key Takeaways -- Key Takeaways -- Onward: Exploring the AI PM Roles -- Part II AI PM Specializations -- Chapter 5 AI-Experiences PM: Shaping User Interaction with AI -- Key Responsibilities: Shaping the AI User Experience -- Day-to-Day Activities -- Required Skills and Knowledge: The AI-Experiences PM Toolkit -- Core Product Management Craft and Practices -- Engineering Foundations for PMs -- Essential Leadership and Collaboration Skills -- AI Lifecycle and Operational Awareness -- Illustrative Example: A Day in the Life of an AI-Experiences PM."]
[{"$nulls":4},"Challenges and Complexities -- How the AI-Experiences PM Interacts with Other Roles -- Chapter Summary and Key Takeaways -- Key Takeaways -- Onward: Architecting the AI Foundation -- Chapter 6 AI-Builder PM: Architecting the Foundation of Intelligent Systems -- Key Responsibilities: Building and Managing the AI Foundation -- Day-to-Day Activities -- Required Skills and Knowledge: The AI-Builder PM's Technical and Strategic Toolkit -- Core Product Management Craft and Practices -- Engineering Foundations for PMs -- Essential Leadership and Collaboration Skills -- AI Lifecycle and Operational Awareness -- Illustrative Example: A Day in the Life of an AI-Builder PM -- Challenges and Complexities -- How the AI-Builder PM Interacts with Other Roles -- Chapter Summary and Key Takeaways -- Key Takeaways -- Onward: Supercharging the PM Workflow -- Chapter 7 AI-Enhanced PM: Supercharging Product Management with AI -- Key Responsibilities: Augmenting PM Workflows and Decision-Making with AI -- Day-to-Day Activities -- Required Skills and Knowledge: The AI-Enhanced PM's Toolkit -- Core Product Management Craft and Practices -- Engineering Foundations for PMs -- Essential Leadership and Collaboration Skills -- AI Lifecycle and Operational Awareness -- Illustrative Example: A Day in the Life of an AI-Enhanced PM -- Examples of AI Tools -- Challenges and Complexities -- How the AI-Enhanced PM Interacts with Other Roles -- Skill Comparison: AI-Experiences PM, AI-Builder PM, and AI-Enhanced PM -- Chapter Summary and Key Takeaways -- Key Takeaways -- Onward: From Theory to Action -- Part III Connecting the Dots Between AI/ML Knowledge and PM Craft -- Chapter 8 Identifying and Evaluating AI "]
[{"$nulls":4},"Opportunities -- Uncovering Potential Use Cases-Mining Your Product for AI Gold -- Recognizing Data-Rich Problem Areas -- Analyzing Existing Data Sources."]
[{"$nulls":4},"Asking the Right Questions -- AI/ML Capability Matching: Connecting Problems to Solutions -- Understanding Your AI/ML Toolkit: Key Capabilities -- Matching Capabilities to Problems: A Practical Approach -- Finding AI Opportunities in the User Journey -- Mapping the User Journey: Charting the Course -- Identifying Pain Points and Opportunities: The AI Detective Work -- Applying AI/ML to Enhance Touchpoints: The Transformation -- Feature Enhancement Through AI/ML-Transforming Existing Functionality -- Identifying Enhancement Opportunities: Finding the Weak Spots -- Applying AI/ML to Enhance Features: The Transformation Process -- Proactive Product Management-Anticipating User Needs with AI -- Understanding the Power of Prediction and Automation -- Key Areas for Predictive and Automation Opportunities -- Identifying Opportunities: A Practical Approach -- Responsible AI Foundations-Ethical and Feasibility Considerations -- Ethical Considerations: The \"Do No Harm\" Principle -- Feasibility Considerations: Can We Actually Build This? -- Practical Ideation Techniques for AI/ML Use Cases-Thinking Like an AI-First Product Manager -- Ideation Techniques: Unleashing Your AI Creativity -- Cultivating an AI-First Mindset -- Chapter Summary and Key Takeaways -- Key Takeaways -- Onward: Measuring the Value of Your Ideas -- Chapter 9 ROI Calculation for AI Projects: Measuring the Impact and Demonstrating Value -- From Model Performance to Business Impact: A PM's Guide to AI Metrics -- Defining AI/ML-Specific Metrics: The Foundation for Measuring ROI -- The Importance of Baselines: Knowing Where You Started -- Understanding the Confusion Matrix: Decoding Classification Performance -- Key Performance Metrics for AI/ML Models: Beyond the Confusion Matrix -- Context Matters: Selecting the Right Metrics for Your AI/ML Application -- Important Considerations."]
[{"$nulls":4},"End-to-End Example-Predicting Churn in a Subscription Service -- 1. Identify the Business Goal: Defining the \"Why\" -- 2. Define the AI/ML Application and Solution -- 3. Identify Data Sources and Engineer Features: The Raw Materials -- 4. Select the Metrics: Defining Success -- 5. Establish Baseline Metrics: Setting the Starting Point -- 6. Conduct Model Training and Evaluation: Building and Testing the AI -- 7. Conduct A/B Testing: Measuring Real-World Impact -- 8. Calculate the Results and ROI: Quantifying the Value -- 9. Monitor and Maintain the Model for Long-Term Success -- A/B Testing for AI and ML Projects: Validating Impact and Optimizing Performance -- What Is A/B Testing (in a Nutshell)? -- Why Is A/B Testing Especially Important for AI/ML? -- How to Conduct A/B Testing for AI and ML: A Step-by-Step Guide -- Key Considerations for AI/ML A/B Testing -- Chapter Summary and Key Takeaways -- Key Takeaways -- Onward: From the Lab to a Live Product -- Chapter 10 Building and Deploying AI Solutions: From Lab to Live -- MLOps: The Key to Reliable and Scalable AI -- Key Components of MLOps-The AI Production Line -- CI/CD, IaC, and Collaboration: The Foundational Pillars of MLOps -- Glossary of Key MLOps Terms -- MLOps End-to-End Example: Churn Prediction in a Subscription Service (Product Manager's Perspective) -- Chapter Summary and Key Takeaways -- Key Takeaways -- Onward: Building with Integrity -- Chapter 11 Responsible AI and Ethical Considerations: Building AI with Integrity -- Understanding AI Bias and Fairness: The Foundation of Responsible AI -- Identifying Potential Biases: Where Bias Can Creep In -- Mitigating Potential Biases: A Proactive Approach -- Protected Classes and AI Fairness-Designing for Inclusion -- What are "]
[{"$nulls":4},"Protected Classes? -- Why Focus on Protected Classes? (The Legal and Ethical Imperative)."]
[{"$nulls":4},"How Protected Classes Relate to AI Bias: The Mechanisms of Discrimination."]
[null,null,null,"Sommario/riassunto","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."]