Managing AI Deployment Projects, Part 1

Übersicht

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.

Lehrplan

• CPMAI Overview and the Seven Patterns of AI
This module serves as the introduction to the first of two courses on managing AI infrastructure deployment projects, focusing specifically on elements unique to this domain following the CPMAI methodology. You will define what constitutes an AI deployment project, identify the unique characteristics of such projects, and explore the "Seven Patterns of AI."
• CPMAI Phase I: Business Understanding
Establishing the right foundation for an AI project starts with clearly defining the business problem you intend to solve. In this module, you will learn why AI is needed, set success metrics, identify the relevant pattern(s) of AI, clarify the scope, determine whether the AI project can proceed, and ensure that stakeholders agree on goals.
• CPMAI Phase II: Data Understanding
In this module, you will learn what data is needed and whether it is sufficient in quantity and quality. Successful AI efforts depend on having the right data, at the right time, in the right format. Phase II is designed to confirm that such data is actually available, feasible to work with, and suitable for solving the stated business problems.
• CPMAI Phase III: Data Preparation
Now that you have already confirmed that the business problem warrants an AI solution (Phase I) and that you have identified and inventoried the data needed to power that solution (Phase II). Now comes the phase where the bulk of practical effort often occurs. Many teams discover that 80% or more of their time on AI projects is spent preparing data rather than coding or modeling. By systematically planning and executing data preparation, you maximize the chances that your AI project will succeed.
• Managing AI Deployment Projects, Part 1 Wrap-up
This final module synthesizes the foundational work of the CPMAI methodology, reviewing the Seven Patterns of AI and CPMAI Phases I through III. This marks the conclusion of the Part 1 in the course series, providing the essential blueprint needed to transition into Part 2.

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Unterrichtet von

Moshe Gotesman

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Fachgebiete

Artificial Intelligence