CPMAICertified Professional in Managing AI
Course Overview
With artificial intelligence redefining how organizations innovate and deliver value, professionals must master both AI fundamentals and structured project methodologies. The Certified Professional in Managing AI (CPMAI™) certification, offered through the PMI, provides a globally recognized framework that merges project management best practices with AI-specific lifecycle models.
The aim of this c
Key Takeaways
Course Outline
• Overview of Artificial Intelligence and Cognitive Technologies
• Definitions, Terminology, and Core Concepts
• Key Differences Between Traditional Software and AI Projects
• AI Business Use Cases and Market Applications
• Foundations of Cognitive Computing, NLP, CV, and Predictive Analytics
• Overview of CPMAI™ Methodology and Phases (I–VI)
• Phase I: Business Understanding – Strategic Alignment and Value Drivers
• Phase II: Data Understanding – Identifying, Profiling, and Exploring Data
• Phase III: Data Preparation – Cleaning, Structuring, and Labeling
• Phase IV: Model Development – Choosing and Training AI Models
• Phase V: Evaluation – Validating Model Accuracy and Relevance
• Phase VI: Deployment – Operationalization, Monitoring, and Governance
• Iterative Process Management and Phase Transitions
• CPMAI Templates, Use Cases, and Best Practices
• Overview of Machine Learning Concepts and Lifecycle
• Supervised vs. Unsupervised Learning Models
• Model Training, Testing, and Validation Techniques
• Overfitting, Bias, and Model Optimization
• Performance Metrics and Interpretability
• Data Requirements in AI Projects
• Evaluating Data Quality, Completeness, and Volume
• Metadata, Taxonomy, and Feature Engineering
• Data Governance, Compliance, and Security
• Structuring Data Pipelines for ML Readiness
• Project and Program Management in AI Contexts
• Aligning AI Projects with Organizational Portfolios
• Change Management and Cross-functional Team Coordination
• Scheduling, Resourcing, and Budgeting AI Programs
• Monitoring Risks, Dependencies, and Stakeholder Communication
• Principles of Responsible and Ethical AI
• Identifying and Mitigating Bias in Data and Models
• Fairness, Transparency, and Explainability in AI
• Risk Management, Accountability, and Governance Structures
• Documentation and Regulatory Considerations
Who Should Attend?
AI Project Managers
Data Scientists
Machine Learning Engineers
Digital Transformation Leaders
IT Project Leads
Program Managers
PMO Analysts
Business Analysts
Product Owners
Risk & Compliance Officers
Ethics Officers
Public Sector Decision Makers