Abstract

Machine Learning Operations (MLOps) is a set of practices and technologies designed to automate and manage the complete lifecycle of machine learning models, from development and training to deployment, monitoring, and retraining. As machine learning models are increasingly integrated into real-world applications, manually deploying and maintaining them can result in inconsistent environments, delayed updates, performance degradation, and difficulties in detecting data or model drift. MLOps frameworks address these challenges by combining machine learning workflows with DevOps principles such as continuous integration, continuous delivery, automation, version control, and observability. The study highlights how combining experiment management, CI/CD pipelines, containerization, model serving, and monitoring can improve the reproducibility, reliability, scalability, and maintainability of machine learning systems.