Motivation for AI Engineering Development Process
Artificial intelligence (AI) applications often involve not only classical application engineering but also elements of research. Sometimes it is not clear from the start which approach will be better and one needs to conduct experiments to evaluate multiple approaches. For example, if we are building a machine learning model we would need to evaluate and experiment with different features until we find an optimal feature set.
Furthermore, if we are building machine learning models, usually debugging is not an easy task. Also in many cases, it is not trivial to evaluate the performance of statistical models and how this performance will translate to business value. All these factors can add an additional layer of complexity that the engineering teams need to cope with.