CPMAI Phase 3 | Data Preparation for AI Projects: The Make-or-Break Step

Описание: Most AI projects don’t fail because of the model… they fail because of the data.
In this episode, we break down CPMAI Phase 3: Data Preparation in a practical, project-manager-friendly way—so you can run AI initiatives with clarity, control, and confidence.

What you’ll learn in CPMAI Phase 3 (Data Preparation)

✅ Data Cleansing (quality fixes that prevent garbage-in/garbage-out)
✅ Data Engineering (pipelines, integration, transformations, reliability)
✅ Data Privacy & Security (access control, compliance mindset, safe handling)
✅ Data Labelling (HITL, QA, consistency—how to avoid noisy labels)
✅ Data Selection (choose the right data, reduce bias, improve performance)
✅ Shortcuts with GenAI (where GenAI helps—and where it creates risk)
✅ Data Prep Pipelines (repeatable, auditable, production-ready workflows)
✅ Data Augmentation (boost training signal without breaking realism)

Who this is for

Project Managers managing AI/ML or GenAI projects

Product owners, analysts, data teams working with real-world messy data

Anyone exploring CPMAI and wanting a clean Phase-by-Phase learning path

Quick retention challenge (mini-quiz)

Comment below: Which is the biggest risk in Phase 3—Bad data quality, weak labeling, or missing privacy controls?

You will be able to answer CPMAI exam questions by understanding these concepts !

Series continuity (so you don’t miss the flow)

This episode fits right after:
Phase 1: Business Understanding → Phase 2: Data Understanding → Phase 3: Data Preparation → (next: Model Development)

This video is an educational breakdown aligned with common CPMAI Phase 3 concepts to help professionals manage AI projects better.

👍 If you’re managing AI projects, save this video and share it with your team—Phase 3 is where delivery success is decided.

#CPMAI #AIProjectManagement #DataPreparation