A Systematic Review of MLOps Tools: Tool Adoption, Lifecycle Coverage, and Critical Insights

TL;DR

This systematic review analyzes MLOps tools, mapping their functions and coverage across the lifecycle, highlighting prevalent usage trends, benefits, limitations, and the importance of interoperability.

Contribution

It provides a comprehensive mapping of MLOps tools to lifecycle components and insights into their adoption, scope, and integration challenges.

Findings

Orchestration frameworks, data versioning, experiment tracking, and cloud platforms are most common.

No single tool covers the entire MLOps lifecycle, leading to tool combination.

Interoperability is crucial for effective MLOps pipelines.

Abstract

Machine Learning Operations (MLOps) has become increasingly critical as more organisations move ML models into production. However, the growing landscape of MLOps solutions has introduced complexity for practitioners trying to select appropriate tools. To investigate how and why these tools are adopted in practice, this paper conducts a systematic review of the academic literature focused on MLOps tools. We map tools to MLOps lifecycle components to reveal their function, scope, and the challenges they are designed to address. We identify usage trends and synthesise reported benefits and limitations. The most commonly used components, according to the findings, are orchestration frameworks, data versioning, experiment tracking, and managed cloud platforms. No single tool covers the entire lifecycle, so researchers often combine multiple tools to build complete pipelines. This highlights the importance of interoperability across MLOps tools in real-world MLOps pipelines.