Overview

AI, Data Science & Cloud Certificates from Google, IBM & Meta — 50% Off

One plan covers every Professional Certificate on Coursera. 50% off Coursera Plus Annual for 10 days only — price increases June 17.

Unlock All Certificates

This course introduces the PMI Cognitive Project Management for AI (CPMAI) framework, providing you with a structured, repeatable methodology for successfully planning, managing, and executing complex AI projects. You will gain the practical expertise needed to navigate the entire AI development lifecycle. You will gain practical skills in aligning AI initiatives with business objectives, assessing data feasibility, preparing and managing robust data pipelines, efficiently developing and evaluating AI models, and finally, operationalizing AI systems for long-term success. Completing this course provides a significant competitive advantage. You will not only master project management principles tailored to AI, but also learn to embed trustworthy AI practices—covering governance, ethics, and continuous monitoring—into every phase of development. What makes this program unique is its practical, real-world orientation. Unlike traditional project management courses, this program is data-centric, iterative, and vendor-neutral. This ensures you can immediately apply the CPMAI framework across diverse industries, technologies, and organizational contexts. By the end of this specialization, you will be equipped with a structured, repeatable methodology needed to reduce AI project failure rates and drive sustainable business value via new AI infrastructure. This positions you as a critical leader in today's demanding, AI-powered world.

Syllabus

Course Introduction and Part 1 Refresher

This module serves as the introduction to Part 2 of two courses on managing AI infrastructure deployment projects, focusing specifically on elements unique to this domain following the CPMAI methodology. You will recall and explain key concepts from Part 1, including the seven patterns of AI and CPMAI Phases I–III (Business Understanding, Data Understanding, Data Preparation).

CPMAI Phase IV: Model Development

At this point, your AI project transitions from the foundational data-centric work of the earlier phases toward creating, testing, and refining a working AI or ML model. This phase includes selecting the right tools and algorithm(s), training and tuning the model, deciding how best to leverage off-the-shelf or pretrained models if needed, and systematically tracking experiments so you can iterate effectively.

CPMAI Phase V: Model Evaluation

In CPMAI Phase V, you will evaluate the AI solution’s performance from both a technical and business perspective before deciding if it is ready for large-scale deployment. This phase ensures that the AI solution is accurate, aligned with organizational goals, and robust enough to handle changing data or conditions over time.

CPMAI Phase VI: Model Operationalization

In CPMAI Phase VI: Model Operationalization, you will integrate your validated AI solutions into the organization’s systems and workflows, ensuring the AI solution consistently delivers value and can adapt to inevitable changes in data, objectives, or real-world conditions. The process is sometimes referred to as “putting AI into operation or production” or simply “deployment,” but CPMAI goes further by addressing continuous integration, monitoring, governance, and user adoption needs.

Organizational Roles and Skills

Successfully delivering AI initiatives requires more than just technology and tools. It involves a cross-functional team, a culture that embraces data-centric thinking, and committed executive sponsorship. CPMAI recognizes that AI projects are iterative and data-driven efforts, and organizations need to align the right people, skill sets, and leadership support to execute these projects effectively.