AI Development Education 1 article

Developing AI-enabled applications requires an understanding of models, data, interfaces, evaluation, and the constraints of deployment. The subject spans development environments, data preparation, model interfaces, evaluation, and integration into software workflows; learners encounter distinctions between training a model and calling an existing one, along with testing and safety considerations. Prototype testing, model evaluation, and safe deployment decisions connect introductory model concepts to the practice of shipping an AI-enabled application.