Principles and Practices of the Generative AI Life Cycle

This course provides a comprehensive exploration of the generative AI (GenAI) life cycle, offering students a robust understanding of the key principles and processes involved in developing, deploying, and maintaining GenAI models. Designed to provide a theoretical foundation, the course emphasizes the strategic aspects of each phase in the GenAI life cycle, ensuring participants gain a nuanced perspective of how generative AI evolves from concept to deployment and beyond.Students begin by exploring the GenAI life cycle, understanding its phases, and grasping why effective management is crucial to ensuring both operational success and ethical integrity. This introductory section establishes a baseline for the more detailed discussions to come, guiding participants through the various roles that stakeholders play and the essential governance frameworks that maintain alignment with regulatory standards and organizational goals.The journey continues with an in-depth analysis of problem identification and requirement gathering. Here, students learn the importance of aligning AI capabilities with business objectives, as well as the techniques for collecting and validating functional requirements with relevant stakeholders. The focus on these initial phases emphasizes the significance of groundwork in ensuring GenAI projects are goal-oriented and feasible.As students move into the stages of data collection and preparation, they engage with the critical role that data plays in training effective GenAI models. Topics such as data sourcing, quality assurance, and ethical considerations ensure participants develop a deep awareness of the complexities involved in data management for AI. The course introduces students to preprocessing techniques essential for transforming raw data into valuable training inputs, reinforcing the importance of careful preparation in achieving desired outcomes.In subsequent sections, the course delves into the intricacies of model design, selection, and optimizatio