Machine learning operations landscape: platforms and tools

Lisana Berberi, Valentin Kozlov, Giang Nguyen, Judith Sáinz-Pardo Díaz, Amanda Calatrava, Germán Moltó, Viet Tran, and Álvaro López García. Machine learning operations landscape: platforms and tools. Artificial Intelligence Review, 58:167, 3 2025.

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Abstract

As the field of machine learning advances, managing and monitoring intelligent models in production, also known as machine learning operations (MLOps), has become essential. Organizations are increasingly adopting artificial intelligence as a strategic tool, thus increasing the need for reliable, and scalable MLOps platforms. Consequently, every aspect of the machine learning life cycle, from workflow orchestration to performance monitoring, presents both challenges and opportunities that require sophisticated, flexible, and scalable technological solutions. This research addresses this demand by providing a comprehensive assessment framework of MLOps platforms highlighting the key features necessary for a robust MLOps solution. The paper examines 16 MLOps tools widely used, which revolve around capabilities within AI infrastructure management, including but not limited to experiment tracking, model deployment, and model inference. Our three-step evaluation framework starts with a feature analysis of the MLOps platforms, then GitHub stars growth assessment for adoption and prominence, and finally, a weighted scoring method to single out the most influential platforms. From this process, we derive valuable insights into the essential components of effective MLOps systems and provide a decision-making flowchart that simplifies platform selection. This framework provides hands-on guidance for organizations looking to initiate or enhance their MLOps strategies, whether they require an end-end solutions or specialized tools.

BibTeX Entry

@article{Berberi2025mlo,
   abstract = {
As the field of machine learning advances, managing and monitoring intelligent models in production, also known as machine learning operations (MLOps), has become essential. Organizations are increasingly adopting artificial intelligence as a strategic tool, thus increasing the need for reliable, and scalable MLOps platforms. Consequently, every aspect of the machine learning life cycle, from workflow orchestration to performance monitoring, presents both challenges and opportunities that require sophisticated, flexible, and scalable technological solutions. This research addresses this demand by providing a comprehensive assessment framework of MLOps platforms highlighting the key features necessary for a robust MLOps solution. The paper examines 16 MLOps tools widely used, which revolve around capabilities within AI infrastructure management, including but not limited to experiment tracking, model deployment, and model inference. Our three-step evaluation framework starts with a feature analysis of the MLOps platforms, then GitHub stars growth assessment for adoption and prominence, and finally, a weighted scoring method to single out the most influential platforms. From this process, we derive valuable insights into the essential components of effective MLOps systems and provide a decision-making flowchart that simplifies platform selection. This framework provides hands-on guidance for organizations looking to initiate or enhance their MLOps strategies, whether they require an end-end solutions or specialized tools.
},
   author = {Lisana Berberi and Valentin Kozlov and Giang Nguyen and Judith Sáinz-Pardo Díaz and Amanda Calatrava and Germán Moltó and Viet Tran and Álvaro López García},
   doi = {10.1007/s10462-025-11164-3},
   issn = {1573-7462},
   issue = {6},
   journal = {Artificial Intelligence Review},
   month = {3},
   pages = {167},
   title = {Machine learning operations landscape: platforms and tools},
   volume = {58},
   url = {https://link.springer.com/10.1007/s10462-025-11164-3},
   year = {2025}
}