After preparing high-quality data and training the model on such data, the ML lifecycle reaches its final stage: deploying the trained model in a production environment. This stage poses significant challenges due to the diverse and heterogeneous nature of the production environment. As described by Hong et al.  [102], a production environment typically involves a varied group of model consumers, including not only model developers but also domain experts, program managers, and business stakeholders. This diversity introduces complexity in achieving their goals, as the requirements of these consumers may differ significantly. Taking a resume screening model as an example, model developers primarily prioritize accuracy, while business stakeholders may value fairness more.