Udemy - CPMAI™ Tutoring Masterclass 1st round

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.
Who this course is for
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
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