ComputingComputer ScienceArtificial IntelligenceMachine LearningSoftware EngineeringArtificial Intelligence AdoptionArtificial Intelligence GovernanceDevopsResponsible AiSoftware Development Methodology
DevOps practices have increasingly been applied to software development as well as the machine learning lifecycle, in a process known as MLOps. Currently, many professionals have written about this topic, but still few results can be found in the academic and scientific literature on MLOps and how to to implement it effectively. Considering aspects of responsible AI, this number is even lower, opening up a field of research with many possibilities. This article presents five steps to guide the understanding and adoption of MLOps in the context of responsible AI. The study aims to serve as a reference guide for all those who wish to learn more about the topic and intend to implement MLOps practices to develop their systems, following responsible AI principles.
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A Survey of DevOps Concepts and Challenges
ACM Computing Surveys · 2019 · 370 citations · Full text
Responsible AI—Two Frameworks for Ethical Design Practice
Dorian Peters, Karina Vold, Diana Robinson et al. · IEEE Transactions on Technology and Society · 2020 · 282 citations · Full text