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MLOps Tool Technology Review: Analysis of Support for the Machine Learning Model Lifecycle and Application Recommendations

Helder Rodrigo Pinto

ISLA — Instituto Politécnico de Gestão e Tecnologia

Journal of Pioneering Artificial Intelligence Research, 01 01 2026

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 complementary 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 prototyping 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.

Keywords

#MLOps #MLflow #experiment tracking #model versioning #reproducibility #algorithmic fairness #CI/CD integration #permission control #tool recommendation #regulated environments #Computer science #Scalability #software engineering #Permission

Bibliographic Details

Publication Type Journal article

Publication Year 2026

Journal / Conference / Work Journal of Pioneering Artificial Intelligence Research

ISSN 3069-0846

ISLA Authors & Affiliations

MLOps Tool Technology Review: Analysis of Support for the Machine Learning Model Lifecycle and Application Recommendations

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Keywords

Bibliographic Details

ISLA Authors & Affiliations