(PMI-CPMAI™) Exam Prep: Managing AI Projects with Confidence

About Course

Prepare for the PMI-CPMAI™ certification with a practical, exam-focused approach to managing AI initiatives. This course covers data-centric project management, AI lifecycle planning, governance, risk management, and real-world implementation strategies, equipping professionals to successfully lead AI projects from concept to deployment.

Understand the CPMAI methodology and how it differs from traditional project management
Explain why AI projects fail and how to prevent common failure patterns
Define AI project scope in environments with uncertainty and learning
Assess AI feasibility and risk before major investments
Evaluate data readiness, data quality, and ground truth
Manage data labeling, pipelines, and quality controls
Align AI initiatives with business value and ROI
Select the right AI pattern for different business problems
Apply AI-specific metrics across the project lifecycle
Make Go / No-Go decisions using data-driven criteria

Course Content

Module 1 — Foundations of AI Project Management

Lecture 01 — Introduction to PMI CPMAI Certification

00:00
Lecture 02 — CPMAI Framework & Methodology Overview

00:00
Lecture 03 — Why AI Projects Need Specialized Project Management

00:00

Module 2 — AI Lifecycle & Business Alignment

Module 3 — Feasibility, Scope & Success Definition

Module 4 — Data Management for AI Projects

Module 5 — Model Strategy & Development

Module 6 — Evaluation, Risk & Reliability

Module 7 — Deployment, Governance & Compliance

Module 8 — Exam Preparation & Application

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(PMI-CPMAI™) Exam Prep: Managing AI Projects with Confidence

About Course

What Will You Learn?

Course Content

Module 1 — Foundations of AI Project Management

Lecture 01 — Introduction to PMI CPMAI Certification

Lecture 02 — CPMAI Framework & Methodology Overview

Lecture 03 — Why AI Projects Need Specialized Project Management

Module 2 — AI Lifecycle & Business Alignment

Lecture 04 — AI Lifecycle vs Traditional Project Lifecycle

Lecture 05 — Business Value, ROI, and AI Strategy

Lecture 06 — AI Business Analysis and Governance

Lecture 07 — AI Patterns and Business Matching

Module 3 — Feasibility, Scope & Success Definition

Lecture 08 — AI Feasibility and Risk Assessment

Lecture 09 — AI Project Scope and Success Metrics

Module 4 — Data Management for AI Projects

Lecture 10 — Data Readiness and Data Quality

Lecture 11 — Training Data and Ground Truth

Lecture 12 — Data Governance and Privacy

Lecture 13 — Data Cleaning and Transformation

Lecture 14 — Data Labeling and AI Pipelines

Lecture 15 — Data Quality Controls

Module 5 — Model Strategy & Development

Lecture 16 — Model Strategy: Build vs Buy

Lecture 17 — Model Training and Fine-Tuning

Lecture 18 — AI Agents and Emerging Trends

Module 6 — Evaluation, Risk & Reliability

Lecture 19 — Model Evaluation and Performance Metrics

Lecture 20 — Bias, Fairness, and AI Testing

Lecture 21 — Model Drift and Monitoring

Module 7 — Deployment, Governance & Compliance

Lecture 22 — AI Deployment and MLOps

Lecture 23 — AI Governance and Compliance

Lecture 24 — AI Lifecycle and Continuous Improvement

Module 8 — Exam Preparation & Application

Lecture 25 — Exam Preparation and Case Study

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