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  3. Volume 86, no.4, 2025
  4. MLFLOW DESIGN CONCEPTS IN CONTAINERIZED AND CLOUD-NATIVE SYSTEMS

MLFLOW DESIGN CONCEPTS IN CONTAINERIZED AND CLOUD-NATIVE SYSTEMS

ISTCMTM.
2025;
Volume 86(4), Number 4
: 84-90
https://doi.org/10.23939/istcmtm2025.04
Authors:
  1. Yevhen Bershchankyi
  2. Halyna Klym
1
Lviv Polytechnic National University
2
Lviv Politechnic National University, Ukraine

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. The results demonstrate that leveraging cloud-native tools and services enables dynamic orchestration, improved resource utilization, and enhanced model lifecycle automation. A case study confirms that the proposed design improved experiment tracking scalability and reduced deployment complexity, achieving higher system resilience and maintainability. These outcomes highlight the effectiveness of applying cloud-native design principles to MLFlow.

MLFlow
MLOps
containerization
Cloud-Native Systems
Kubernetes
Model Lifecycle
Model Registry.
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