Master the Cognitive Project Management for AI

What you'll learn

The fundamentals and structure of the CPMAI methodology

The six phases of CPMAI and how they map to CRISP-DM and agile practices

How to initiate, plan, execute, and monitor AI projects using CPMAI

Practices for managing data, models, ethics, and business outcomes

How to avoid common AI project pitfalls and ensure alignment with stakeholder expectations

Knowledge Checkpoints to pass the CPMAI certification exam

Requirements

Take CPMAI Training at PMI

Description

This tutoring course is designed based on the below guideline on how to learn and prepare for the CPMAI certification.1. Understand What CPMAI IsCPMAI is not a technical AI certification — it's about managing AI and cognitive technology projects.It combines traditional project management (like PMI/PMBOK) with CRISP-DM and best practices for AI projects.You need to show you understand:How AI projects are different from normal IT projects.How to apply CPMAI methodology stages to an AI project.2. Study the CPMAI MethodologyThere are 6 CPMAI stages (based on CRISP-DM but tailored for AI projects):Business UnderstandingData UnderstandingData PreparationModelingEvaluationDeploymentFor each stage, you must know:What happens at that stage.Key deliverables.Common challenges (especially in AI — like bias, data drift, explainability).3. Review CPMAI Key ThemesCPMAI emphasizes:Iterative cycles (not one-and-done).AI Ethics (bias, transparency, fairness).Explainability (XAI) — how to make AI models understandable.Risk management specific to AI projects (e.g., data risk, model risk).4. Use the CPMAI Study MaterialsIf you enrolled in an official course, they usually provide:CPMAI Handbook or methodology guide (core reading).Templates (for deliverables at each stage).Sample exam questions (hugely important).TIP: Make your own notes on each CPMAI stage. Summarize:InputsActivitiesOutputsKey risks or considerations5. Practice with ScenariosThe exam tends to give realistic project scenarios and ask you:"At which CPMAI stage are you?""What should you do next?""What is missing in the project?"TIP: Practice identifying stages and decisions based on given case studies.6. Know AI Basics (but not in technical depth)You should be comfortable with basic concepts like:What is supervised vs unsupervised learning?What is overfitting?What is a model drift?What is explainability vs transparency?TIP: You don't need to code or build models. You just need to manage AI projects intelligently.

Overview

Section 1: Introduction

Lecture 1 Welcome

Section 2: AI Fundamentals

Lecture 2 Understanding AI Fundamentals and Evolution – AI Fundamentals

Lecture 3 Evaluating AI Applications and Patterns – AI Fundamentals

Lecture 4 Seven Patterns of AI – AI Fundamentals

Lecture 5 Applying Machine Learning Fundamentals – AI Fundamentals

Section 3: CPMAI Methodology

Lecture 6 Differentiating AI Project Management Approaches

Lecture 7 Executing the Business Understanding Phase (CPMAI Phase I)

Lecture 8 Managing the Data Understanding Phase (CPMAI Phase II)

Lecture 9 Coordinating the Data Preparation Activities (CPMAI Phase III)

Lecture 10 Determining the Approaches for Model Development (CPMAI Phase IV)

Lecture 11 Conducting Model Evaluation and Maintenance (CPMAI Phase V)

Section 4: ML for AI

Lecture 12 Applying Classification and Clustering Algorithms

Lecture 13 Implementing Neural Networks and Deep Learning

Lecture 14 Leveraging Generative AI and Large Language Models (LLMs)

Lecture 15 Selecting Machine Learning Tools and Platforms

Section 5: Data for AI

Lecture 16 Managing Data Fundamentals and Big Data Concepts

Lecture 17 Implementing Data Governance and Management

Lecture 18 Engineering Data Pipelines for AI

Lecture 19 Executing Data Preparation and Transformation

Section 6: Managing AI

Lecture 20 Evaluating Model Performance and Accuracy

Lecture 21 Deploying AI Models for Production

Section 7: Trustworthy AI

Lecture 22 Ethical, Responsible, and Trustworthy AI

Lecture 23 Implementing AI Privacy and Security

Lecture 24 Ensuring AI Transparency and Explainability

Lecture 25 Navigating AI Regulations and Frameworks

Project managers working on or transitioning to AI/data initiatives,Data scientists and engineers seeking a project delivery framework,Business analysts and consultants in AI transformation,Technology leaders who need to align AI projects with strategic goals,Anyone preparing for CPMAI certification