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).

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.

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.

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.

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.