The lifecycle of a generative AI project and the core technologies
The lifecycle for developing and deploying generative AI solutions spans multiple stages, with some variations from traditional ML projects, such as model customization and model evaluation. While certain phases like use case definition and data preparation align closely, stages including model development, training, evaluation, and adaptation take on unique characteristics for generative models.
Figure 15.1: Generative AI project lifecycle
At a high level, a generative AI project consists of a series of stages, including identification of business use cases, model selection or pre-training, domain adaptation and model customization, post-customization model evaluation, and model deployment. It’s important to recognize that while a generative AI project places significant emphasis on the capabilities and quality of the model itself, the model constitutes just one facet within the broader development...
