Building an AI model is only half the battle. Getting that model into production quickly, safely, and at scale is where the real challenge begins. Johnny Santiago Valdez Calderon has spent years helping organizations turn promising prototypes into reliable products. His approach focuses on clarity, repeatability, and smart automation. The goal is simple. Reduce friction and deliver value faster. 1. Start With a Clean Development Path Every strong AI deployment pipeline begins with an organized development environment. Calderon stresses that teams need consistent project structures, clear naming conventions, and well documented practices. These basics reduce confusion and help engineers avoid wasting time on avoidable errors. He recommends creating a shared template for new machine learning projects. This template should include necessary folders, configuration files, logging tools, and code structure. When everyone builds from the same foundation, collaboration becomes smoother and on...