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Video course · 15 chapters · 130 min · certificate

CPMAI Masterclass: Six Phases & ECO Domains Explained

Prepare for CPMAI: why AI projects need their own methodology, the seven patterns of AI, the trustworthy AI framework, and each of the six phases from business understanding to operationalization.

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What you'll learn

  • Explain why AI projects need the CPMAI methodology
  • Identify the seven patterns of AI
  • Apply the trustworthy AI framework
  • Run business understanding with feasibility gates
  • Handle data understanding and preparation
  • Develop, evaluate and operationalize models

Chapters

15 chapters · 130:28
  1. 4:29 01Intro Members CPMAI certification overview What the CPMAI certification is and who it's for.
  2. 4:54 02Why Members Why CPMAI exists Conventional project methods fall short for AI; CPMAI adds a data-centric layer.
  3. 4:50 03Delivery Members Different from conventional projects CPMAI layers over agile, predictive or hybrid delivery.
  4. 2:54 04Exam Members Exam focus areas Three study areas: the six phases, the seven patterns of AI and the trustworthy AI framework.
  5. 5:56 05Patterns Members Seven patterns of AI The seven patterns, such as hyper-personalization, used to classify AI projects.
  6. 3:15 06Trust Members Trustworthy AI principles Explaining data sources and decisions to stakeholders and governing bodies.
  7. 4:14 07Trust Members The trustworthy AI framework The five components of trustworthy AI, starting with ethical AI, mapped across all six phases.
  8. 12:41 08Phase 1 Members Phase 1: problem definition Business understanding starts with defining the problem and checking whether AI is the right tool.
  9. 12:00 09Phase 1 Members Phase 1: feasibility and gates Deterministic versus probabilistic systems, feasibility, and gates to pause or kill projects.
  10. 8:56 10Phase 2 Members Phase 2: data understanding Identifying and validating data, including big data characteristics and regulations.
  11. 7:09 11Phase 2 Members Phase 2: data quality and exit Six data quality dimensions and the exit criteria before moving on.
  12. 17:19 12Phase 3 Members Phase 3: data preparation Turning raw data into structured, usable formats for modelling.
  13. 12:55 13Phase 4 Members Phase 4: model development Building models once data preparation checkpoints are cleared.
  14. 8:37 14Phase 5 Members Phase 5: model evaluation Validating performance, reliability and business alignment before deployment.
  15. 20:19 15Phase 6 Members Phase 6: operationalization Moving the model into production with monitoring and human oversight.

Jobs this course suits

Our AI checked this course against 500 jobs; these get the most out of it. Each job links to its learning path.

IT Project ManagersProgram ManagersProject ManagersAI ConsultantsTechnology ManagersProduct ManagersCDOs (Chief Digital Officers)Manager of ITsChief Product Officers (CPOs)CIOs (Chief Information Officers)Chief Digital Officers (CDOs)Innovation Strategists