Manage AI Model Development and Evaluation

In the PMI-CPMAI framework, Manage AI Model Development and Evaluation covers Phases IV (Model Development) and V (Model Evaluation) of the CPMAI methodology. It follows Business Understanding, Data Understanding, and Data Preparation, and comes before Model Operationalization. The focus is on how a project manager guides the creation and validation of AI models so they meet business objectives, technical standards, and responsible AI requirements. The manager does not need to code the models personally. During model development, the project manager works with data scientists and engineers to choose suitable approaches. These may include supervised, unsupervised, or reinforcement learning, generative AI, pretrained or foundation models, or simpler rule-based options when AI is not justified. Key activities include defining model requirements, selecting algorithms, planning training and testing data splits, managing hyperparameter tuning, and tracking experiments. The manager also handles compute resources, timelines, and budgets. Because AI development is iterative and experimental, CPMAI stresses short, agile cycles and realistic expectations, since early models may not perform well. Decisions about building, buying, or reusing models are made here. Model evaluation checks whether the model is fit for purpose. Technical metrics include accuracy, precision, recall, F1 score, and error rates. These are assessed alongside business KPIs and the success criteria set in Phase I. Evaluation also looks for overfitting and underfitting, generalization to new data, and robustness. Trustworthy AI factors are examined as well, including bias and fairness, explainability, transparency, privacy, security, and regulatory compliance. The project manager arranges stakeholder reviews, documents results, and runs go/no-go decisions. If a model falls short, CPMAI encourages iterating back to earlier phases. For example, the team may gather better data or reframe the problem rather than force deployment. Good practices include version control, reproducibility, model documentation such as model cards, and clear handoff criteria for operationalization. Overall, this domain makes sure AI models are technically sound, ethically responsible, and deliver measurable business value before they move into production. In the PMI-CPMAI framework, Manage AI Model Development and Evaluation covers Phases IV (Model Development) and V (Model Evaluation) of the CPMAI methodology. It follows Business Understanding, Data Understanding, and Data Preparation, and comes before Model Operationalization. The focus is on how …