MLFLOW DESIGN CONCEPTS IN CONTAINERIZED AND CLOUD-NATIVE SYSTEMS
This article presents the main findings from an in-depth study of MLFlow design concepts in containerized and cloud-native systems. The research focuses on how MLFlow, as a core MLOps framework, can be efficiently deployed and managed in cloud- native environments to ensure scalable, reproducible, and secure machine learning workflows. The study analyzes the architectural principles and integration patterns of MLFlow components: Tracking Server, Projects, Models, and Registry within distributed containerized infrastructures.