MLOps, or Machine Learning Operations, is a practice that bridges the gap between data science and operations, aiming to streamline and automate the lifecycle of machine learning models. It facilitates rapid deployment, monitoring, and lifecycle management of ML models that predict market trends, detect fraudulent transactions, and customize client services. ML-Ops is becoming increasingly ubiquitous in the modern era of banking. This presentation briefly demonstrates how SR-Bank uses ML-Ops to improve efficiency, security, and time-to-market.