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Review Protocol: A systematic literature review of MLOps

  • MLOps have become an increasingly important topic in the deployment of machine learning in production. While Machine Learning Operations was predominantly used as a buzzword for methods in Machine Learning (ML) for the time being, since 2019, they are increasingly used in the context of deploying ML algorithms. This report is a protocol for a systematic literature review (SLR) that aims to determine the MLOps terminology and identify related activities. A further goal of the SLR is to identify where MLOps can be linked to classical software engineering. In addition, related automation techniques are considered. The projected literature review aims to draw conclusions from papers that explicitly use the term MLOps or Machine Learning Operations with the objective to provide the necessary common baseline for future MLOps research and practice. This report thoroughly documents the SLR method, processes, and data material. We also gathered all relevant data to comprehend MLOps fully. Through our comprehensive analysis, we hope to provide valuable insights and recommendations for optimizing MLOps practices.

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Author:Leonhard Faubel, Klaus Schmid
Series (Serial Number):Hildesheimer Informatik-Berichte (1/2023, SSE 2/23/E)
Document Type:Report
Year of Completion:2023
Publishing Institution:Stiftung Universität Hildesheim
Release Date:2023/08/30
Tag:MLOps; Machine Learning; Machine Learning Operations
Page Number:33
PPN:Link zum Katalog
Institutes:Fachbereich IV
Licence (German):License LogoCreative Commons - Namensnennung - Nicht kommerziell 4.0