An Analysis of MLOps Architectures: A Systematic Mapping Study

TL;DR

This systematic mapping study provides a comprehensive overview of MLOps architectures, categorizing components, variants, and tools to guide both researchers and practitioners in designing effective MLOps systems.

Contribution

It offers the first systematic categorization of MLOps architecture components, variants, and their associated tools based on a thorough review of 43 studies.

Findings

Categorized 35 MLOps architecture components

Described multiple MLOps architecture variants

Mapped components to existing MLOps tools

Abstract

Context. Despite the increasing adoption of Machine Learning Operations (MLOps), teams still encounter challenges in effectively applying this paradigm to their specific projects. While there is a large variety of available tools usable for MLOps, there is simultaneously a lack of consolidated architecture knowledge that can inform the architecture design. Objective. Our primary objective is to provide a comprehensive overview of (i) how MLOps architectures are defined across the literature and (ii) which tools are mentioned to support the implementation of each architecture component. Method. We apply the Systematic Mapping Study method and select 43 primary studies via automatic, manual, and snowballing-based search and selection procedures. Subsequently, we use card sorting to synthesize the results. Results. We contribute (i) a categorization of 35 MLOps architecture components, (ii) a description of several MLOps architecture variants, and (iii) a systematic map between the identified components and the existing MLOps tools. Conclusion. This study provides an overview of the state of the art in MLOps from an architectural perspective. Researchers and practitioners can use our findings to inform the architecture design of their MLOps systems.