A Systematic Review of MLOps Tools: Tool Adoption, Lifecycle Coverage, and Critical Insights
For practitioners selecting MLOps tools, this review provides a structured overview of tool adoption and gaps, but it is an incremental survey without novel empirical results.
This systematic review maps MLOps tools to lifecycle components, finding that no single tool covers the entire lifecycle and that orchestration, data versioning, experiment tracking, and managed cloud platforms are most commonly used, highlighting the need for interoperability.
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.