Microsoft AI Product Manager Professional Certificate course Syllabus

Full curriculum breakdown — modules, lessons, estimated time, and outcomes.

Overview: This professional certificate program is designed for product managers and aspiring AI product leaders to gain the skills needed to drive AI-powered product innovation. The curriculum blends foundational AI concepts with practical product management strategies, emphasizing real-world applications, collaboration with technical teams, and responsible AI practices. With a total time commitment of approximately 12–16 weeks at a suggested pace of 3–5 hours per week, learners will progress through structured modules that build from core principles to hands-on application via a final project. Lifetime access allows flexible, self-paced learning.

Module 1: Foundations of AI Product Management

Estimated time: 12 hours

Understanding the role of an AI Product Manager
Differences between traditional and AI-driven products
Key responsibilities in AI product lifecycle
Real-world case studies of successful AI products

Module 2: AI Technologies and Capabilities

Estimated time: 16 hours

Introduction to machine learning concepts
Overview of natural language processing (NLP)
Basics of computer vision applications
Data requirements and model training fundamentals
Integrating AI services into product ecosystems

Module 3: Building and Managing AI Products

Estimated time: 12 hours

Defining product vision and AI value propositions
Translating business requirements into AI features
Creating AI product roadmaps and prioritization frameworks
Collaborating with data scientists and engineering teams

Module 4: AI Model Lifecycle and Deployment

Estimated time: 10 hours

Stages of the AI model lifecycle
Model evaluation metrics and performance monitoring
Deployment considerations for production environments
Working with MLOps and technical stakeholders

Module 5: Responsible AI and Product Governance

Estimated time: 8 hours

Principles of fairness, bias, and transparency in AI
Privacy and regulatory compliance considerations
Implementing AI governance frameworks
Monitoring AI systems for risk and performance

Module 6: Final Project