You have recently employed XGBoost to train a Python-based model designed for online serving. Your model prediction service will be accessed by a backend service built in Golang, operating on a Google Kubernetes Engine (GKE) cluster. Your model necessitates both pre-processing and post-processing steps, which must be executed during serving. Your primary objectives are to minimize code alterations, reduce infrastructure maintenance, and expedite the deployment of your model into a production environment. What steps should you take to accomplish these goals?