Project Management – Managing with AI – Class Outline
Chapter 1: Introduction
- Why Take the CPMAI Exam?
- How to use the materials
- Other materials to use to Study
- The PMI-CPMAI Workbook
- Cognitive Project Management in AI
- Course structure and highlights
- The 5 Domains, The 6 Phases, and the 7 AI Patterns
- AI Fundamentals
Chapter 2: The Need for AI Project Management
- Introduction
- Why AI Now?
- The Seven patterns of AI
- Why AI projects fail
- Fears and concerns of trustworthy AI
- The layers of trustworthy AI
- Iterative and agile approaches for AI
- Cognitive project management in AI
- CPMAI: Iterative, Six-Phase Approach
- PMI-CPMAI Workbook Checkpoint
- Summary
Chapter 3: Matching AI With Business Needs
- Introduction
- Determine the problem you are solving and if AI is a good fit
- Evaluate AI feasibility
- Map business problems to ai patterns
- Determine ai go/no-go
- Determine ai project ROI and success metrics
- Scope and schedule ai projects
- Determine needs for ai project team
- Determine project-specific ai risks
- How it all maps to CPMAI phase I
- PMI-CPMAI Workbook Checkpoint
- Summary
Chapter 4: Identifying Data Needs for AI Projects
- Introduction
- The role of data in AI
- Determine data quality and quantity requirements for AI
- Determine data sets for AI projects
- Understand data privacy, compliance, and access requirements
- Coordinate data infrastructure and access needs
- Analytics and key data roles
- How it all maps to CPMAI phase II
- PMI-CPMAI Workbook Checkpoint
- Summary
Chapter 5: Managing Data Preparation Needs for AI Projects
- Introduction
- Data preparation for AI projects
- Data pipeline in AI projects
- Data quality check and verification
- Data transformation and synthetic data
- Data augmentation and labeling for AI
- Data management for generative ai systems
- Trustworthy AI in data preparation
- How it all maps to CPMAI phase III
- PMI-CPMAI Workbook Checkpoint
- Summary
Chapter 6: Iterating Development and Delivery of AI Projects
- Introduction
- Machine learning and models
- Model development
- Model validation
- Building generative AI systems
- How it all maps to CPMAI phase IV
- PMI-CPMAI Workbook Checkpoint
- Summary
Chapter 7: Testing and Evaluating AI Systems
- Introduction
- Model evaluation
- Model iteration
- Model performance, and data and model drift
- Evaluating models against business and technology KPIs
- AI system monitoring and management
- Explainable and interpretable AI systems
- How it all maps to CPMAI phase V
- PMI-CPMAI Workbook Checkpoint
- Summary
Chapter 8: Operationalizing AI
- Introduction
- Moving AI models into operation
- AI platforms and infrastructure
- Ways to interact with AI models
- Operationalizing generative AI
- Model life cycle management
- AI and model governance
- Trustworthy AI considerations in operation
- Limits of AI
- How it all maps to CPMAI phase VI
- PMI-CPMAI Workbook Checkpoint
- Summary
Chapter 9: Putting It All Together
- Exam Preparation Questions
- Before You Take the Exam
- Tricks for Preparation, Taking, and Passing the Exam
Please note: The topics above represent the material typically covered in this class based on the class materials and class length. Topics are subject to change based on how the class is progressing through the topics during the allocated class time.