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."

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.

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.

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.

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.