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NewPMI-CPMAI
PMI Certified Professional in Managing AI (PMI-CPMAI)™
Description
Lead AI initiatives end-to-end using the PMI Certified Professional in Managing AI (PMI-CPMAI)™ methodology. In this hands-on, instructor-led workshop, you’ll learn how to scope AI opportunities, define success measures, work through data-centric delivery, and plan for deployment, monitoring, and governance across iterative cycles.
PMI CPMAI Certification Training Information
Benefits
- Learn PMI’s CPMAI v7 framework to manage AI initiatives from problem framing through operationalization.
- Apply the six CPMAI phases: Business Understanding, Data Understanding, Data Preparation, Model Development, Model Evaluation, and Model Operationalization.
- Build confidence in data sourcing, quality, labeling, governance, and pipelines that make AI solutions reliable and repeatable.
- Learn how to evaluate models, plan deployment, monitor performance, and manage drift so AI solutions deliver measurable business value.
- Incorporate ethics, privacy, security, transparency, and organizational controls into your AI delivery approach.
What to Expect
- Exam Prep Course: This course is designed to help you prepare for a certification exam. You can expect coverage of all exam learning objectives, with a focus on understanding key concepts, terminology, and how they’re assessed.
- Content Pace: Certification courses are fast-paced so that all exam objectives are covered. Instructors adhere to a stricter schedule so that everything is covered. While discussions and examples are included, the focus remains on certification readiness.
- Outside of Class Study Time: Plan for self-study outside of class, especially if the content is new to you. Passing the exam is not guaranteed.
Audience
This course is intended for project managers, product managers, PMO leaders, business analysts, consultants, and technology professionals who support or lead AI initiatives and want a practical, end-to-end framework for delivering AI solutions.
Prerequisites
- There are no formal prerequisites required to attend this course or to sit for the PMI-CPMAI (CPMAI v7) exam. The course begins with AI fundamentals and progresses through the CPMAI methodology from Business Understanding to Model Operationalization.
- Recommended Background: Familiarity with project delivery concepts (scope, stakeholders, outcomes) and basic data/analytics vocabulary is helpful. No coding is required; technical depth is introduced at a management level with practical examples and exercises.
PMI CPMAI Certification Training Outline
The Need for AI Project Management
- Why AI now?
- 7 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
Matching AI With Business Needs: CPMAI Phase I – Business Understanding
- 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 the AI project team
- Determine project-specific AI risks
- Learn how all this maps to CPMAI Phase I
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
- Learn how all this maps to CPMAI Phase II
Managing Data Preparation Needs for AI Projects: CPMAI Phase III – Data Preparation
- Data preparation for AI projects
- Data pipeline in AI projects
- Data quality checks 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
Iterating Development and Delivery of AI Projects: CPMAI Phase IV – Model Development
- Machine learning and models
- Model development
- Model validation
- Building generative AI systems
- Mapping AI development to CPMAI Phase IV
Testing and Evaluating AI Systems: CPMAI Phase V – Model Evaluation
- 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
- Mapping to CPMAI Phase V
Operationalizing AI: CPMAI Phase VI – Model Operationalization
- Moving AI models into operation
- AI platforms and infrastructure
- Ways to interact with AI models
- Operationalizing generative AI
- Model lifecycle management
- AI and model governance
- Trustworthy AI considerations in operation
- Limits of AI
- Moving to the next iteration after CPMAI Phase VI
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$3145.00
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3 Days Course |