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Mastering AI Product Management

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Getting Started

Course Overview

Infrastructure and Tools for Building AI Products

Introduction to Infrastructure and Tools

Definitions: What Is and Is Not AI

Machine Learning vs. Deep Learning – Understanding the Difference

Learning Types and Supervised Learning Models

Semi-supervised, Unsupervised, and Reinforcement Learning Models

The Order—Flow and Process

Data Containers and Pipelines

Managing Projects—IaaS

Deployment Strategies

Succeeding in AI

The Promise of AI

Model Development and Maintenance for AI Products

Introduction to Model Development and Maintenance

Understanding the Stages of NPD

Performance Metric: R-Squared Metric

Model Deployment

Testing and Troubleshooting

Refreshing: Updating Models

Machine Learning and Deep Learning Deep Dive

Introduction to Machine Learning and Deep Learning The Old: Exploring ML The New: Exploring DL A Brief History of DL Types of Neural Networks: Part I Types of Neural Networks: Part II Emerging Technologies Explainability and Accuracy Summary Quiz

Commercializing AI Products

Introduction to Commercializing AI Products AI Products in the B2B Market AI Products in the B2C Market Blue Ocean Products in the AI Market Red Ocean Products in the AI Market Marketing Strategies and Explainability Summary Quiz

AI Transformation and Its Impact on Product Management

Introduction to AI Transformation and Its Impact Money and Value Goods and Services Government and Autonomy Sickness and Health Basic Needs Summary Quiz

Understanding the AI-Native Product

Introduction to AI-Native Product Stages of AI Product Development AI/ML Product Dream Team: Part I AI/ML Product Dream Team: Part II Investing in Tech Stack Productizing AI-Powered Outputs AI Customization Selling AI: Product Management Summary Quiz

Productizing the ML Service

Introduction to Productizing the ML Services AI vs. Traditional Software Product Difference Between AI and Traditional Software Products: Part I Difference Between AI and Traditional Software Products: Part II B2B vs. B2C—Productizing Business Models: Part I B2B vs. B2C—Productizing Business Models: Part II Consistency and AIOps/MLOps Performance Evaluation Feedback Loop Summary Quiz

Customization for Verticals, Customers, and Peer Groups

Introduction to Customization Orienting AI toward Specific Areas Market Benefits Building Our AI Product Strategy Verticals: FinTech, Healthcare, and Cybersecurity Anomaly Detection and UEBA Value Metrics and Key Performance Indicators Thought Leadership: Learning from Peer Groups Summary Quiz

Macro and Micro AI for Our Product

Introduction to Micro and Macro AI Macro AI: Foundations and Umbrellas Machine Learning Computer Vision and NLP Deep Learning Robotics, Expert Systems, and Fuzzy Logic in Macro AI Micro AI: Feature Level Robotics, Expert System, and Fuzzy Logic in Micro AI Successes: Examples that Inspire Challenges: Common Pitfalls Summary Quiz

Benchmarking Performance, Growth Hacking, and Cost

Introduction to Benchmarking, Growth Hacking, and Cost Value Metrics A Guide to North Star Metrics KPIs and Other Metrics OKRs and Product Strategy Hacking: Product-Led Growth The Tech Stack CDPs and CEPs for Customer Success Tools for Communication, Analytics, Testing, and Data Warehousing Growth-Hacking Tools and Managing Costs and Pricing Summary Quiz

The Rising Tide of AI

Introduction to Rising Tide of AI Evolve or Die: When Change Is the Only Constant The Fourth Industrial Revolution From Consultants to Building Teams for AI Adoption Embracing Challenges in AI Product Management Navigating Risks in AI Product Management Summary Quiz

Trends and Insights Across Industry

Introduction to Trends and Insights Across Industry Exploring AI Growth: Trends, Ethics, and Applied Strategies Exploring AI Growth: Creative Applications and Future Trends Trends in AI Adoption AI Revolution: Surge of Creative and Autonomous Development Succeeding in AI Adoption Summary Quiz

Evolving Products into AI Products

Introduction to Evolving Products into AI Products Balancing Possibilities, Realities and Long-Term Product Value Prioritizing AI: Value, Scope, and Customer Impact The Power Behind AI Product Data Data for AI Success Competitive Landscape for AI Integration Creating a Strategic Roadmap for AI Integration Refining the Strategic Roadmap for AI Integration Navigating AI Transformation: Red Flags and Green Flags Summary Quiz

Conclusion

Quiz

Test yourself on model development and maintenance for AI products.

1

Which factor contributes to the degradation or drift of AI models over time?

A)

Uninterrupted training process.

B)

Consistent data hygiene practices.

C)

Stable user demographics.

D)

Changes in reported data format or new data categories.

Question 1 of 100 attempted

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