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MLOps :
MLOps : A Taxonomy and a Methodology
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- Testi, Matteo (author)
- Integrated Research Centre, Università Campus Bio-Medico di Roma, Rome, Italy; DeepLearningItalia, Bergamo, Italy
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- Ballabio, Matteo (author)
- DeepLearningItalia, Bergamo, Italy
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- Frontoni, Emanuele (author)
- VRAI Laboratory, Department of Political Sciences Communication and International Relations, Università Degli Studi di Macerata, Macerata, Italy
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- Iannello, Giulio (author)
- Department of Engineering, Unit of Computer Systems and Bioinformatics, Università Campus Bio-Medico di Roma, Rome, Italy
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- Moccia, Sara (author)
- The BioRobotics Institute, Scuola Superiore Sant-Anna, Pisa, Italy; Department of Excellence in Robotics and AI, Scuola Superiore Sant-Anna, Pisa, Italy
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- Soda, Paolo (author)
- Umeå universitet,Radiofysik,Department of Engineering, Unit of Computer Systems and Bioinformatics, Università Campus Bio-Medico di Roma, Rome, Italy
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- Vessio, Gennaro (author)
- Department of Computer Science, Università Degli Studi di Bari Aldo Moro, Bari, Italy
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(creator_code:org_t)
- Institute of Electrical and Electronics Engineers (IEEE), 2022
- 2022
- English.
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In: IEEE Access. - : Institute of Electrical and Electronics Engineers (IEEE). - 2169-3536. ; 10, s. 63606-63618
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Abstract
Subject headings
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- Over the past few decades, the substantial growth in enterprise-data availability and the advancements in Artificial Intelligence (AI) have allowed companies to solve real-world problems using Machine Learning (ML). ML Operations (MLOps) represents an effective strategy for bringing ML models from academic resources to useful tools for solving problems in the corporate world. The current literature on MLOps is still mostly disconnected and sporadic. In this work, we review the existing scientific literature and we propose a taxonomy for clustering research papers on MLOps. In addition, we present methodologies and operations aimed at defining an ML pipeline to simplify the release of ML applications in the industry. The pipeline is based on ten steps: business problem understanding, data acquisition, ML methodology, ML training & testing, continuous integration, continuous delivery, continuous training, continuous monitoring, explainability, and sustainability. The scientific and business interest and the impact of MLOps have grown significantly over the past years: the definition of a clear and standardized methodology for conducting MLOps projects is the main contribution of this paper.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Keyword
- continuous delivery
- continuous integration
- continuous monitoring
- continuous training
- MLOps
- sustainability
- XAI
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