Abstract

This paper critically evaluates MLflow, an open-source MLOps tool for managing the lifecycle of machine learning models. It emphasizes its strengths in experiment tracking, model versioning, and modularity, which support reproducibility and scalability across various frameworks and cloud providers.

However, it points out crucial limitations such as the lack of native monitoring (data drift, fairness), detailed permission control and full integration with CI/CD orchestrators. These shortcomings require complementa ry tools, increasing complexity and operating costs, especially for regulated environments or those with less technical maturity.

In conclusion, MLflow is recommended for small and medium-sized companies, or teams that need rapid pro totyping and flexibility. For large organizations or regulated sectors, although useful in the initial phase, it requires the integration of other tools for complete and robust management of the model lifecycle.

DOI: doi.org/10.63721/26JPAIR0123

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