PMI Certified Professional in Managing AI (PMI-CPMAI)™

Course 3604

Duration: 3 days
Language: English
21 PMI PDUs
Level: Intermediate

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.

This course’s content is aligned to the official PMI-CPMAI curriculum, and includes the PMI on-demand course and exam bundle, plus a 1-year membership to PMI.

PMI CPMAI Certification Training Delivery Methods

In-Person
Online
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PMI CPMAI Certification Training Information

Benefits
- This is a Learning Tree authored, instructor-led course that follows the official PMI-CPMAI curriculum and incorporates PMI’s on-demand bundle (including the exam voucher) as part of the overall learning package.
- 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. For a deeper, more hands-on learning experience, consider Risk Management Training for Projects, Programs, and Operations.
- 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 & Prerequisites
- Who Should Attend: 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.
Certification & Exam Information
- Certification Alignment: This Learning Tree instructor-led course is aligned to the PMI Certified Professional in Managing AI (PMI-CPMAI)™ / CPMAI v7 exam domains: AI Fundamentals, CPMAI Methodology, Machine Learning, Data for AI, Managing AI, and Trustworthy AI.
- Certification Expectations: This course includes the official PMI-CPMAI On-Demand course, plus as exam voucher and 1 year of PMI membership. Passing the exam is not guaranteed.
- Exam Details: The CPMAI v7 exam exam includes 100 questions total (including 10 unscored pre-test questions) with 2 hours (120 minutes) to complete the exam and no scheduled break. You may attempt the exam up to three times within the 90-day eligibility period (per PMI policy). Currently, CPMAI v7 does not require PDUs or renewal to maintain the credential once earned.

PMI CPMAI Certification Training Outline

Learning Objectives

Required Pre‑Course Preparation

Ensure you have access to your PMI and My Learning Tree portals before the start of class. Reach out to Learning Tree customer service if you are having trouble.

Module 1: 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

Module 2: 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

Module 3: 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

Module 4: 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

Module 5: 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

Module 6: 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

Module 7: 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

Select Commercial or Government Pricing

$3,145

3-day instructor-led training course
One-on-one after-course instructor coaching

Would you like to take this course as a team?

Dec 9 - 11 8:00 AM - 5:00 PM EST

Virtual
Jan 20 - 22 8:00 AM - 5:00 PM EST

Ottawa or Virtual
Mar 10 - 12 8:00 AM - 5:00 PM EST

Herndon, VA or Virtual
Apr 21 - 23 8:00 AM - 5:00 PM EDT

Ottawa or Virtual
Jun 9 - 11 8:00 AM - 5:00 PM EDT

Herndon, VA or Virtual
Jul 21 - 23 8:00 AM - 5:00 PM EDT

Ottawa or Virtual
Sep 8 - 10 8:00 AM - 5:00 PM EDT

Herndon, VA or Virtual
Oct 20 - 22 8:00 AM - 5:00 PM EDT

Ottawa or Virtual

Scroll to view additional course dates

$3,145

3-day instructor-led training course
One-on-one after-course instructor coaching

Would you like to take this course as a team?

Dec 9 - 11 8:00 AM - 5:00 PM EST

Virtual
Jan 20 - 22 8:00 AM - 5:00 PM EST

Ottawa or Virtual
Mar 10 - 12 8:00 AM - 5:00 PM EST

Herndon, VA or Virtual
Apr 21 - 23 8:00 AM - 5:00 PM EDT

Ottawa or Virtual
Jun 9 - 11 8:00 AM - 5:00 PM EDT

Herndon, VA or Virtual
Jul 21 - 23 8:00 AM - 5:00 PM EDT

Ottawa or Virtual
Sep 8 - 10 8:00 AM - 5:00 PM EDT

Herndon, VA or Virtual
Oct 20 - 22 8:00 AM - 5:00 PM EDT

Ottawa or Virtual

Scroll to view additional course dates

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PMI CPMAI Certification Training FAQs

Earning your PMI-CPMAI certification is a big step—maintaining it should be simple. Ongoing learning keeps your skills sharp and your expertise relevant in a constantly evolving field.

To maintain certification, PMI-CPMAI holders must earn 30 professional development units (PDUs) every 3 years. Each PDU represents one hour spent:

Learning
Teaching others
Presenting
Reading
Volunteering
Creating content