DevOps has become increasingly widespread, with companies employing its methods in different fields. In this context, MLOps automates Machine Learning pipelines by applying DevOps practices. Considering the high number of tools available and the high interest of the practitioners to be supported by tools to automate the steps of Machine Learning pipelines, little is known concerning MLOps tools and their functionalities. To this aim, we conducted a Multivocal Literature Review (MLR) to (i) extract tools that allow for and support the creation of MLOps pipelines and (ii) analyze their main characteristics and features to provide a comprehensive overview of their value. Overall, we investigate the functionalities of 13 MLOps Tools. Our results show that most MLOps Tools support the same features but apply different approaches that can bring different advantages, depending on user requirements.
A Multivocal Literature Review of MLOps Tools and Features
Fabiano Pecorelli;
2022-01-01
Abstract
DevOps has become increasingly widespread, with companies employing its methods in different fields. In this context, MLOps automates Machine Learning pipelines by applying DevOps practices. Considering the high number of tools available and the high interest of the practitioners to be supported by tools to automate the steps of Machine Learning pipelines, little is known concerning MLOps tools and their functionalities. To this aim, we conducted a Multivocal Literature Review (MLR) to (i) extract tools that allow for and support the creation of MLOps pipelines and (ii) analyze their main characteristics and features to provide a comprehensive overview of their value. Overall, we investigate the functionalities of 13 MLOps Tools. Our results show that most MLOps Tools support the same features but apply different approaches that can bring different advantages, depending on user requirements.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
